Appendix C: Agricultural Insurance Loss and Relationships to Climate across the Inland Pacific Northwest Region of the United States
Erich Seamon, Paul E. Gessler, John T. Abatzoglou, Philip Mote, Stephen Lee
September 2019
Dissertation Appendix C
Chapter 2: Regression based random forest modeling of inland pacific northwest (iPNW) drought-related wheat insurance loss, using time lagged climate correlation matrix assocation
Appendix C documents Chapter 2 analyses related to agricultural commodity loss, at a county level, from 2001-2015, across the 24 county region of the Inland Pacific Northwest (IPNW). In this analysis, we use a time lagged matrix approach to examine a range of combinations of climate and insurance loss for wheat due to drought.
Our analysis can be described in 5 specific steps:
Step 1. We subset insurance loss claims by wheat due to drought
Step 2. We aggregate climate data by month and county for the 24 county study area
Step 3. We construct a design matrix of all monthly climate combinations, per county and per climate variable.
Step 4. We select the monthly combination that has the highest correlation with wheat/drought claims, per county, for each year (2001 to 2015). Based on the output, we assemble an optimized climate/insurance loss dataset for all counties.
Step 5. We examine conditional relationships of climate to insurance loss using regression based rand forest analysis
Step 1. Examining wheat/drought acreage ratios compared to average precipitation, potential evapotranspiration, and aridity for the iPNW study area, 2001 to 2015
In step 1, we perform a broad examination of wheat insurance loss due to drought for our 24 county iPNW study area, by calculating the ratio of wheat/drought acreage claims vs. all other wheat related damage cause claims, and comparing that data to individual climate variables. Example: For Whitman County, WA - we calculated the total amount of insurance claim acreage for wheat due to drought (for 2001 to 2015 combined), and divided that by the total amount of wheat insurane acreage for all other damage causes (excessive moisture, hail, frost, freeze, etc). We did this for each of the 24 counties.
We then calculate a summary value for each climate variable (plus aridity - which is PR / PET), for each county, from 2001 to 2015. We then constructed three scatterplots for comparison. Each point represents a county. For precipitaiton and potential evapotranspiration, we use the average annual total.
ipnw_climate_county_comparison <- function(var_commodity, var_damage) {
library(plotrix)
library(ggplot2)
library(gridExtra)
setwd("/dmine/data/USDA/agmesh-scenarios/Idaho/summaries")
temp = list.files(pattern = "_summary")
myfiles = lapply(temp, read.csv, header = TRUE)
ziggy.df <- do.call(rbind , myfiles)
ID1 <- aggregate(.~countyfips + year, ziggy.df, sum)
ID1 <- aggregate(.~countyfips, ID1, mean)
setwd("/dmine/data/USDA/agmesh-scenarios/Washington/summaries")
temp = list.files(pattern = "_summary")
myfiles = lapply(temp, read.csv, header = TRUE)
ziggy.df <- do.call(rbind , myfiles)
WA1 <- aggregate(.~countyfips + year, ziggy.df, sum)
WA1 <- aggregate(.~countyfips, WA1, mean)
setwd("/dmine/data/USDA/agmesh-scenarios/Oregon/summaries")
temp = list.files(pattern = "_summary")
myfiles = lapply(temp, read.csv, header = TRUE)
ziggy.df <- do.call(rbind , myfiles)
OR1 <- aggregate(.~countyfips + year, ziggy.df, sum)
OR1 <- aggregate(.~countyfips, OR1, mean)
countiesfips <- read.csv("/dmine/data/counties/counties_fips.csv", header = TRUE)
colnames(countiesfips) <- c("countyfips", "county", "state")
OR2 <- merge(countiesfips, OR1, by=("countyfips"))
ID2 <- merge(countiesfips, ID1, by=("countyfips"))
WA2 <- merge(countiesfips, WA1, by=("countyfips"))
#------
setwd("/dmine/data/USDA/agmesh-scenarios/Allstates/")
#setwd(monthdir2)
temp = list.files(pattern = paste("Idaho", "_summary", sep=""))
myfiles = lapply(temp, read.csv, header = TRUE)
ziggy.df <- do.call(rbind , myfiles)
xrange <- as.data.frame(ziggy.df)
xrange$county <- as.character(xrange$county)
xrange$damagecause <- as.character(xrange$damagecause)
xrange_1989 <- subset(xrange, year <= 2000)
xrange_2015 <- subset(xrange, year >= 2001)
xrange_1989$loss <- xrange_1989$acres
xrange_1989$acres <- NA
xrange <- rbind(xrange_2015)
xrange$commodity <- trimws(xrange$commodity)
xrange$county <- trimws(xrange$county)
xrange$damagecause <- trimws(xrange$damagecause)
#--acres for all damage causes aggregated
ID_loss_commodity <- subset(xrange, commodity == var_commodity)
ID_loss_all <- aggregate(ID_loss_commodity$acres, by=list(ID_loss_commodity$county, ID_loss_commodity$state), FUN = "sum")
colnames(ID_loss_all) <- c("county", "state", "all_damagecause_acres")
#-loss and lossperacre for just one damage cause
ID_loss1 <- subset(xrange, commodity == var_commodity & damagecause == var_damage)
ID_loss2 <- aggregate(ID_loss1$loss, by=list(ID_loss1$county, ID_loss1$state), FUN = "sum")
ID_loss2_acres <- aggregate(ID_loss1$acres, by=list(ID_loss1$county, ID_loss1$state), FUN = "sum")
colnames(ID_loss2) <- c("county", "state", "loss")
colnames(ID_loss2_acres) <- c("county", "state", "acres")
ID_loss2$acres <- ID_loss2_acres$acres
ID_loss2$lossperacre <- ID_loss2$loss / ID_loss2$acres
#--
ID_loss_climate <- merge(ID_loss2, ID2, by=c("county", "state"))
ID_loss_climate_2 <- merge(ID_loss_all, ID_loss_climate, by=c("county", "state"))
#---WA
setwd("/dmine/data/USDA/agmesh-scenarios/Allstates/")
#setwd(monthdir2)
temp = list.files(pattern = paste("Washington", "_summary", sep=""))
myfiles = lapply(temp, read.csv, header = TRUE)
ziggy.df <- do.call(rbind , myfiles)
xrange <- as.data.frame(ziggy.df)
xrange$county <- as.character(xrange$county)
xrange$damagecause <- as.character(xrange$damagecause)
xrange_1989 <- subset(xrange, year <= 2000)
xrange_2015 <- subset(xrange, year >= 2001)
xrange_1989$loss <- xrange_1989$acres
xrange_1989$acres <- NA
xrange <- rbind(xrange_2015)
xrange$commodity <- trimws(xrange$commodity)
xrange$county <- trimws(xrange$county)
xrange$damagecause <- trimws(xrange$damagecause)
#--acres for all damage causes aggregated
WA_loss_commodity <- subset(xrange, commodity == var_commodity)
WA_loss_all <- aggregate(WA_loss_commodity$acres, by=list(WA_loss_commodity$county, WA_loss_commodity$state), FUN = "sum")
colnames(WA_loss_all) <- c("county", "state", "all_damagecause_acres")
WA_loss1 <- subset(xrange, commodity == var_commodity & damagecause == var_damage)
WA_loss2 <- aggregate(WA_loss1$loss, by=list(WA_loss1$county, WA_loss1$state), FUN = "sum")
WA_loss2_acres <- aggregate(WA_loss1$acres, by=list(WA_loss1$county, WA_loss1$state), FUN = "sum")
colnames(WA_loss2) <- c("county", "state", "loss")
colnames(WA_loss2_acres) <- c("county", "state", "acres")
WA_loss2$acres <- WA_loss2_acres$acres
WA_loss2$lossperacre <- WA_loss2$loss / WA_loss2$acres
WA_loss_climate <- merge(WA_loss2, WA2, by=c("county", "state"))
WA_loss_climate_2 <- merge(WA_loss_all, WA_loss_climate, by=c("county", "state"))
#--OR
setwd("/dmine/data/USDA/agmesh-scenarios/Allstates/")
#setwd(monthdir2)
temp = list.files(pattern = paste("Oregon", "_summary", sep=""))
myfiles = lapply(temp, read.csv, header = TRUE)
ziggy.df <- do.call(rbind , myfiles)
xrange <- as.data.frame(ziggy.df)
xrange$county <- as.character(xrange$county)
xrange$damagecause <- as.character(xrange$damagecause)
xrange_1989 <- subset(xrange, year <= 2000)
xrange_2015 <- subset(xrange, year >= 2001)
xrange_1989$loss <- xrange_1989$acres
xrange_1989$acres <- NA
xrange <- rbind(xrange_2015)
xrange$commodity <- trimws(xrange$commodity)
xrange$county <- trimws(xrange$county)
xrange$damagecause <- trimws(xrange$damagecause)
#--acres for all damage causes aggregated
OR_loss_commodity <- subset(xrange, commodity == var_commodity)
OR_loss_all <- aggregate(OR_loss_commodity$acres, by=list(OR_loss_commodity$county, OR_loss_commodity$state), FUN = "sum")
colnames(OR_loss_all) <- c("county", "state", "all_damagecause_acres")
OR_loss1 <- subset(xrange, commodity == var_commodity & damagecause == var_damage)
OR_loss2 <- aggregate(OR_loss1$loss, by=list(OR_loss1$county, OR_loss1$state), FUN = "sum")
OR_loss2_acres <- aggregate(OR_loss1$acres, by=list(OR_loss1$county, OR_loss1$state), FUN = "sum")
colnames(OR_loss2) <- c("county", "state", "loss")
colnames(OR_loss2_acres) <- c("county", "state", "acres")
OR_loss2$acres <- OR_loss2_acres$acres
OR_loss2$lossperacre <- OR_loss2$loss / OR_loss2$acres
OR_loss_climate <- merge(OR_loss2, OR2, by=c("county", "state"))
OR_loss_climate_2 <- merge(OR_loss_all, OR_loss_climate, by=c("county", "state"))
#-merge all three states
iPNW_loss_climate <- rbind(OR_loss_climate_2, ID_loss_climate_2, WA_loss_climate_2)
#--subset to only iPNW 24 counties
Franklin <- subset(iPNW_loss_climate, county == "Franklin" & state == "WA")
iPNW_loss_climate <- subset(iPNW_loss_climate, county == "Benewah"
| county == "Latah" | county == "Nez Perce" | county == "Lewis"
| county == "Idaho" | county == "Wasco" | county == "Sherman"
| county == "Gilliam" | county == "Morrow" | county == "Umatilla"
| county == "Union" | county == "Wallowa" | county == "Douglas"
| county == "Walla Walla" | county == "Benton" | county == "Columbia"
| county == "Asotin" | county == "Garfield"
| county == "Grant" | county =="Whitman" | county == "Spokane"
| county == "Lincoln" | county == "Adams" )
iPNW_loss_climate <- rbind(iPNW_loss_climate, Franklin)
iPNW_loss_climate$pct_acreage <- iPNW_loss_climate$acres / iPNW_loss_climate$all_damagecause_acres
#iPNW_loss_climate <- subset(iPNW_loss_climate, year == var_year)
#tmmx
iPNW_loss_climate_tmmx <- iPNW_loss_climate[order(iPNW_loss_climate$tmmx),]
iPNW_loss_climate_tmmx$tmmx <- iPNW_loss_climate_tmmx$tmmx - 273
#not needed#yrangemin <- min(eval(parse(text=paste("iPNW_loss_climate_tmmx$", unit, sep="")))) - (.05 * min(eval(parse(text=paste("iPNW_loss_climate_tmmx$", unit, sep="")))))
#not needed#yrangemax <- max(eval(parse(text=paste("iPNW_loss_climate_tmmx$", unit, sep="")))) + (.05 * max(eval(parse(text=paste("iPNW_loss_climate_tmmx$", unit, sep="")))))
#y2rangemin <- min(iPNW_loss_climate_tmmx$tmmx) - (.05 * min(iPNW_loss_climate_tmmx$tmmx))
#y2rangemax <- max(iPNW_loss_climate_tmmx$tmmx) + (.05 * max(iPNW_loss_climate_tmmx$tmmx))
#twoord.plot(c(1:nrow(iPNW_loss_climate_tmmx)), iPNW_loss_climate_tmmx$pct_acreage, c(1:nrow(iPNW_loss_climate_tmmx)), rylim=c(y2rangemin, y2rangemax), lylim=c(0, 1), iPNW_loss_climate_tmmx$tmmx, mar=c(8,4,4,4), ylab = "% wheat insurance loss acreage due to drought", xticklab=c(" "), rylab = "Mean Max Temperature (C)", type=c("bar", "b"), lcol = "red", rcol = "blue", main = paste(" 2001 - 2015 iPNW % Wheat/drought insurance loss acreage\n compared to mean TMMX", sep=""))
#text(1:nrow(iPNW_loss_climate_tmmx), 0 - .05, srt = 90, adj = 1,
# labels = iPNW_loss_climate_tmmx$county, xpd = TRUE)
#mtext(1, text = "iPNW counties", line = 4, cex = 2)
#mtext(2, text = "% wheat/drought insurance loss acreage", line = 4, cex = 2)
#mtext(4, text = "Annual Mean Precipitation (mm)", line = 1, cex = 2)
#pr
par(mfrow=c(1,3),mar=c(11.8, 6.1, 4, 2.1), family = 'serif', mgp=c(4, 1, 0), las=0)
layout(matrix(c(1,2,3), 1, 3, byrow = TRUE))
iPNW_loss_climate_pr <- iPNW_loss_climate[order(-iPNW_loss_climate$pr),]
iPNW_loss_climate_pr$pr_year <- iPNW_loss_climate_pr$pr * 12
pr <- ggplot(iPNW_loss_climate_pr, aes(x=pr_year, y=pct_acreage)) +
geom_point()+
geom_smooth(method = "loess")+
xlab("Annual Precipitation (mm)") + ylab("Percentage Drought Wheat Acreage Loss") +
theme(text=element_text(size=16, family="serif"))
#yrangemin <- min(iPNW_loss_climate_pr$lossperacre) - 10
#yrangemax <- max(iPNW_loss_climate_pr$lossperacre) + 10
#y2rangemin <- min(iPNW_loss_climate_pr$pr) - (.05 * min(iPNW_loss_climate_pr$pr))
#y2rangemax <- max(iPNW_loss_climate_pr$pr) + (.05 * max(iPNW_loss_climate_pr$pr))
#twoord.plot(c(1:nrow(iPNW_loss_climate_pr)), iPNW_loss_climate_pr$pct_acreage, c(1:nrow(iPNW_loss_climate_pr)), rylim=c(y2rangemin, y2rangemax), lylim=c(0, 1), iPNW_loss_climate_pr$pr, mar=c(8,4,4,4), xticklab=c(" "), type=c("bar", "b"), lcol = "red", rcol = "blue")
#not needed#text(1:nrow(iPNW_loss_climate_pr), 0 - .05, srt = 90, adj = 1, cex = 1.5,
#not needed# labels = iPNW_loss_climate_pr$county, xpd = TRUE)
#mtext(1, text = "iPNW counties", line = 4, cex = 1.5)
#mtext(2, text = "% wheat/drought insurance loss acreage", line = 4, cex = 1.5)
#mtext(4, text = "Annual Mean PR (mm)", line = 1, cex = 1.5)
#mtext(3, text = "2001 - 2015 iPNW % Wheat/drought insurance loss acreage\n compared to annual mean precipitation/county", line = 1, cex = 2)
#pr
#par(mar=c(11.8, 6.1, 4, 2.1), family = 'serif', mgp=c(4, 1, 0), las=0)
#iPNW_loss_climate_pr <- iPNW_loss_climate[order(-iPNW_loss_climate$pr),]
#y2rangemin <- min(iPNW_loss_climate_pr$pr) - (.05 * min(iPNW_loss_climate_pr$pr))
#y2rangemax <- max(iPNW_loss_climate_pr$pr) + (.05 * max(iPNW_loss_climate_pr$pr))
#twoord.plot(c(1:nrow(iPNW_loss_climate_pr)), iPNW_loss_climate_pr$pct_acreage, c(1:nrow(iPNW_loss_climate_pr)), rylim=c(y2rangemin, y2rangemax), lylim=c(0, 1), iPNW_loss_climate_pr$pr, mar=c(8,4,4,4), ylab = "% wheat/drought insurance loss acreage", xticklab=c(" "), rylab = "Annual Mean Precipitation (mm)", type=c("bar", "b"), lcol = "red", rcol = "blue", main = paste("2001 - 2015 iPNW % Wheat/drought insurance loss acreage\n compared to annual mean PR/county", sep=""))
#text(1:nrow(iPNW_loss_climate_pr), 0 - .05, srt = 90, adj = 1,
# labels = iPNW_loss_climate_pr$county, xpd = TRUE)
#mtext(1, text = "iPNW counties", line = 9, cex = 1)
#pet
iPNW_loss_climate_pet <- iPNW_loss_climate[order(iPNW_loss_climate$pet),]
iPNW_loss_climate_pet$pet_year <- iPNW_loss_climate_pet$pet * 12
pet <- ggplot(iPNW_loss_climate_pet, aes(x=pet_year, y=pct_acreage)) +
geom_point()+
geom_smooth() +
xlab("Annual PET (mm)") + ylab("Percentage Drought Wheat Acreage Loss") +
theme(text=element_text(size=16, family="serif"))
#yrangemin <- min(iPNW_loss_climate_pet$lossperacre) - 10
#yrangemax <- max(iPNW_loss_climate_pet$lossperacre) + 10
#y2rangemin <- min(iPNW_loss_climate_pet$pet) - (.05 * min(iPNW_loss_climate_pet$pet))
#y2rangemax <- max(iPNW_loss_climate_pet$pet) + (.05 * max(iPNW_loss_climate_pet$pet))
#twoord.plot(c(1:nrow(iPNW_loss_climate_pet)), iPNW_loss_climate_pet$pct_acreage, c(1:nrow(iPNW_loss_climate_pet)), rylim=c(y2rangemin, y2rangemax), lylim=c(0, 1), iPNW_loss_climate_pet$pet, mar=c(8,4,4,4), xticklab=c(" "), type=c("bar", "b"), lcol = "red", rcol = "blue")
#not needed#text(1:nrow(iPNW_loss_climate_pet), 0 - .05, srt = 90, adj = 1,
#not needed# labels = iPNW_loss_climate_pet$county, xpd = TRUE)
#mtext(1, text = "iPNW counties", line = 4, cex = 1.5)
#mtext(2, text = "% wheat/drought insurance loss acreage", line = 4, cex = 1.5)
#mtext(4, text = "Annual Mean PET (mm)", line = 1, cex = 1.5)
#pr/pet
iPNW_loss_climate_prpet <- iPNW_loss_climate
iPNW_loss_climate_prpet$pet_year <- iPNW_loss_climate_prpet$pet * 12
iPNW_loss_climate_prpet$pr_year <- iPNW_loss_climate_prpet$pr * 12
iPNW_loss_climate_prpet$prpet <- iPNW_loss_climate_prpet$pr_year / iPNW_loss_climate_prpet$pet_year
#iPNW_loss_climate_prpet <- iPNW_loss_climate[order(-iPNW_loss_climate$prpet),]
# ggplot scatterplot
prpet <- ggplot(iPNW_loss_climate_prpet, aes(x=prpet, y=pct_acreage)) +
geom_point()+
geom_smooth()+
xlab("Aridity Index") + ylab("Percentage Drought Wheat Acreage Loss") +
theme(text=element_text(size=16, family="serif"))
grid.arrange(pr, pet, prpet, nrow = 1)
#yrangemin <- min(iPNW_loss_climate_pet$lossperacre) - 10
#yrangemax <- max(iPNW_loss_climate_pet$lossperacre) + 10
#dual axis barplot
#y2rangemin <- min(iPNW_loss_climate_prpet$prpet) - (.05 * min(iPNW_loss_climate_prpet$prpet))
#y2rangemax <- max(iPNW_loss_climate_prpet$prpet) + (.05 * max(iPNW_loss_climate_prpet$prpet))
#twoord.plot(c(1:nrow(iPNW_loss_climate_prpet)), iPNW_loss_climate_prpet$pct_acreage, c(1:nrow(iPNW_loss_climate_prpet)), rylim=c(y2rangemin, y2rangemax), lylim=c(0, 1), iPNW_loss_climate_prpet$prpet, mar=c(8,4,4,4), xticklab=c(" "), type=c("bar", "b"), lcol = "red", rcol = "blue")
#not needed#text(1:nrow(iPNW_loss_climate_prpet), 0 - .05, srt = 90, adj = 1,
#not needed# labels = iPNW_loss_climate_prpet$county, xpd = TRUE)
#mtext(1, text = "iPNW counties", line = 4, cex = 1.5)
#not needed#mtext(2, text = "% wheat/drought insurance loss acreage", line = 4, cex = 1.5)
#mtext(4, text = "Annual Mean Aridity Index (PR/PET (mm))", line = 1, cex = 1.5)
#pdsi - not used
#iPNW_loss_climate_pdsi <- iPNW_loss_climate[order(iPNW_loss_climate$pdsi),]
#not needed#yrangemin <- min(iPNW_loss_climate_pet$lossperacre) - 10
#not needed#yrangemax <- max(iPNW_loss_climate_pet$lossperacre) + 10
#y2rangemin <- min(iPNW_loss_climate_pdsi$pdsi) - (-.25 * min(iPNW_loss_climate_pdsi$pdsi))
#y2rangemax <- max(iPNW_loss_climate_pdsi$pdsi) + (-.5 * max(iPNW_loss_climate_pdsi$pdsi))
#twoord.plot(c(1:nrow(iPNW_loss_climate_pdsi)), iPNW_loss_climate_pdsi$pct_acreage, c(1:nrow(iPNW_loss_climate_pdsi)), rylim=c(y2rangemin, y2rangemax), lylim=c(0,1), iPNW_loss_climate_pdsi$pdsi, mar=c(8,4,4,4), ylab = "% wheat insurance loss acreage due to drought", xticklab=c(" "), rylab = "Mean PDSI", type=c("bar", "b"), lcol = "red", rcol = "blue", main = paste(" 2001 - 2015 iPNW % Wheat/drought insurance loss acreage\n compared to PDSI", sep=""))
#text(1:nrow(iPNW_loss_climate_pdsi), 0 - .05, srt = 90, adj = 1,
# labels = iPNW_loss_climate_pdsi$county, xpd = TRUE)
#mtext(1, text = "iPNW counties", line = 5)
}
ipnw_climate_county_comparison("WHEAT", "Drought")
Step 2. Construction of design matrices of all monthly climate combinations, per county and per climate variable (precipitation, maximum temperature, and potential evapotranspiration)
For the three examined climate variables (potential evapotranspiration, precipitation, maximum temperature), a design matrix was developed for each of the 24 counties that are part of the defined iPNW study area. For each county, an absolute correlation was calculated for each monthly combination for each climate variable to the total wheat insurance loss due to drought. The result is a design matrix map that identifies the monthly combination with the highest correlation For example, for Whitman county, WA, for maximum temperature - the monthly combination with highest correlation between max temperature and wheat insurance loss due to drought was May/June/July (denoted as July 2).
Step 2.1 Maximum temperature design matrix for Whitman County, WA, wheat insurance loss due to drought
state_var <- "WA"
county_var <- "Whitman"
commodity_var <- "WHEAT"
damage_var <- "Drought"
climate_var <- "tmmx"
predictor_var <- "cube_root_loss"
library(gplots)
library(plyr)
library(dplyr)
#library(plotly)
#packageVersion('plotly')
#response may be: loss_month, loss_per_acres, loss_year, total_acres_harvested, and total_acres_loss
#monthlist is jan-dec, each repeated 12 times
monthlist <- as.data.frame(rep(tolower(month.abb), each = 12))
#numlist is 12 months, repeated 12 times
numlist <- as.data.frame(rep((1:12), times = 12))
#monthnumlist puts month names and 1-12 together, which results in a vector of 144 that is the matrix of 12 by 12
monthnumlist <- as.data.frame(cbind(monthlist, numlist))
#renaming columns
colnames(monthnumlist) <- c("month", "monthcode")
#put the monthlist all together in one vector
monthnumlist$combined <- paste(monthnumlist$month, monthnumlist$monthcode, sep="")
#rename the vector
climmonth <- monthnumlist$combined
designmatrix <- matrix(NA, ncol=12, nrow=12)
#create an empty 144 vector to fill up with correlations between loss and climate
dmvector <- as.data.frame(rep(NA, times=144))
cl = 0
for (ppp in climmonth) {
cl = cl +1
setwd("/dmine/data/USDA/agmesh-scenarios/Allstates/climatematrix_rev4")
file1 <- read.csv(paste(state_var, "_", county_var, "_", commodity_var, "_", damage_var, "_", ppp, ".csv", sep=""))
climatevar <- as.data.frame(cbind((file1[climate_var]), file1[2]))
setwd("/dmine/data/USDA/agmesh-scenarios/Allstates/climatematrix_summaries_rev3")
file2 <- as.data.frame(read.csv(paste(state_var, "_", county_var, "_", commodity_var, "_", damage_var, "_", predictor_var, ".csv", sep="")))
file2 <- subset(file2, state == state_var)
file2 <- subset(file2, county == county_var)
colnames(file2) <- c("X", "year", "damagecause", "county", "state", "commodity", predictor_var )
climatevar$zscore <- scale(climatevar[1], center = TRUE, scale = TRUE)
colnames(climatevar[3]) <- "zscore"
kor <- join(climatevar, file2, by = "year")
kor2 <- subset(kor, damagecause != "NA")
colnames(kor2)[3] <- "zscore"
# kor2[9] <- as.numeric(kor2$zscore)
kor3 <- cor(kor2[1], kor2[9])
#insert kor3 into designmatrix iteratively
dmvector[cl,] <- kor3
}
dmvector <- as.data.frame(dmvector)
colnames(dmvector) <- "correlations"
dmvector2 <- (matrix(dmvector$correlations, 12, 12, TRUE) )
dmvector2 <- dmvector2[nrow(dmvector2):1, ]
dmvector3 <- dmvector2[4:12,]
dmvector3[9,4:12] <- NA
dmvector3[8,5:12] <- NA
dmvector3[7,6:12] <- NA
dmvector3[6,7:12] <- NA
dmvector3[5,8:12] <- NA
dmvector3[4,9:12] <- NA
dmvector3[3,10:12] <- NA
dmvector3[2,11:12] <- NA
dmvector3[1,12:12] <- NA
dmvector <- c(dmvector3[9,], dmvector3[8,], dmvector3[7,], dmvector3[6,], dmvector3[5,], dmvector3[4,], dmvector3[3,], dmvector3[2,], dmvector3[1,])
dmvector <- data.frame(dmvector)
if(climate_var=='pr'){
dmv <- which.min( dmvector[1:108,1])
} else {
if(climate_var=='rmin'){
dmv <- which.min( dmvector[1:108,1])
} else {
if(climate_var=='rmax'){
dmv <- which.min( dmvector[1:108,1])
} else {
if(climate_var=='tmmn'){
dmv <- which.max( dmvector[1:108,1])
} else {
if(climate_var=='pet'){
dmv <- which.max( dmvector[1:108,1])
} else {
if(climate_var=='fm100'){
dmv <- which.min( dmvector[1:108,1])
} else {
if(climate_var=='fm1000'){
dmv <- which.min( dmvector[1:108,1])
} else {
if(climate_var=='pdsi'){
dmv <- which.min( dmvector[1:108,1])
} else {
if(climate_var=='tmmx'){
dmv <- which.max( dmvector[1:108,1])
}
}
}
}
}
}
}
}
}
#dmv <- which.max(abs( dmvector[1:108,1]) )
dmv <- as.data.frame(dmv)
colnames(dmv)[1] <- "row"
#dmvector1a <- max(dmvector$correlations)
#dmvector1b <- data.frame(which=dmvector, correlations=dmvector[dmvector1a, ])
monthnumlist2 <- cbind(as.data.frame(c(1:144)), monthnumlist)
colnames(monthnumlist2)[1] <- "row"
monthnumlist3 <- subset(monthnumlist2, row == dmv$row)
#makeRects <- function(tfMat){
require(utils)
cAbove <- expand.grid(1:12, 1:9)[monthnumlist3$row,]
monthzzz <- c("Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep")
my_palette <- colorRampPalette(c("red", "yellow", "green"))(n = 108)
setwd("/dmine/data/USDA/agmesh-scenarios/Allstates/climatematrix_pngs")
# png(filename=paste(state1, "_", county1, "_", damage1, "_", climate_variable12, "_designmatrix.png", sep=""))
lblcol <- c(9:1)
dmvector3a <- round(dmvector3, digits = 2)
newone <<- as.data.frame(expand.grid(1:12, 1:9)[monthnumlist3$row,])
dmvector3a[9,4:12] <- NA
dmvector3a[8,5:12] <- NA
dmvector3a[7,6:12] <- NA
dmvector3a[6,7:12] <- NA
dmvector3a[5,8:12] <- NA
dmvector3a[4,9:12] <- NA
dmvector3a[3,10:12] <- NA
dmvector3a[2,11:12] <- NA
dmvector3a[1,12:12] <- NA
my_palette <- colorRampPalette(c("lightyellow", "yellow", "orange", "darkorange", "red", "darkred"))(n = 32)
nba_heatmap <- heatmap.2(dmvector3, Rowv=NA, Colv=NA, cexCol = 2, cexRow = 2, colsep=1:ncol(dmvector3), rowsep=1:nrow(dmvector3), col=my_palette, symbreaks = FALSE, scale="none", dendrogram = "none", trace="none", labRow = rev(monthzzz), tracecol = "black", key = TRUE, notecol = "black", notecex = 1.5, key.ylab = "", key.xlab = "loss correlation range", key.title = "", main=paste(climate_var, " vs ", predictor_var, "\n", county_var, " County ", state_var, " ", damage_var, "\n", "MAX Correlation in cell: ", monthnumlist3$combined, sep=""))
# nba_heatmap <- heatmap.2(dmvector3, Rowv=NA, Colv=NA, col=topo.colors(16), scale="none", dendrogram = "none", trace="none", labRow = rev(monthzzz), tracecol = "black", key = TRUE, cellnote = dmvector3a, notecol = "black", notecex = 1.5, key.ylab = "", key.xlab = "loss correlation range", key.title = "", main=paste(climate_var, " vs ", predictor_var, "\n", county_var, " County ", state_var, " ", damage_var, "\n", "MAX Correlation in cell: ", monthnumlist3$combined, sep=""), add.expr=points(newone$Var1,newone$Var2, pch=15, cex = 5.5 , col=rgb(199/255, 0, 0, 0.8)))Step 2.2. Potential Evapotranspiration design matrix for Whitman County, WA, wheat insurance loss due to drought
state_var <- "WA"
county_var <- "Whitman"
commodity_var <- "WHEAT"
damage_var <- "Drought"
climate_var <- "pet"
predictor_var <- "cube_root_loss"
library(gplots)
library(plyr)
library(dplyr)
#library(plotly)
#packageVersion('plotly')
#response may be: loss_month, loss_per_acres, loss_year, total_acres_harvested, and total_acres_loss
#monthlist is jan-dec, each repeated 12 times
monthlist <- as.data.frame(rep(tolower(month.abb), each = 12))
#numlist is 12 months, repeated 12 times
numlist <- as.data.frame(rep((1:12), times = 12))
#monthnumlist puts month names and 1-12 together, which results in a vector of 144 that is the matrix of 12 by 12
monthnumlist <- as.data.frame(cbind(monthlist, numlist))
#renaming columns
colnames(monthnumlist) <- c("month", "monthcode")
#put the monthlist all together in one vector
monthnumlist$combined <- paste(monthnumlist$month, monthnumlist$monthcode, sep="")
#rename the vector
climmonth <- monthnumlist$combined
designmatrix <- matrix(NA, ncol=12, nrow=12)
#create an empty 144 vector to fill up with correlations between loss and climate
dmvector <- as.data.frame(rep(NA, times=144))
cl = 0
for (ppp in climmonth) {
cl = cl +1
setwd("/dmine/data/USDA/agmesh-scenarios/Allstates/climatematrix_rev4")
file1 <- read.csv(paste(state_var, "_", county_var, "_", commodity_var, "_", damage_var, "_", ppp, ".csv", sep=""))
climatevar <- as.data.frame(cbind((file1[climate_var]), file1[2]))
setwd("/dmine/data/USDA/agmesh-scenarios/Allstates/climatematrix_summaries_rev3")
file2 <- as.data.frame(read.csv(paste(state_var, "_", county_var, "_", commodity_var, "_", damage_var, "_", predictor_var, ".csv", sep="")))
file2 <- subset(file2, state == state_var)
file2 <- subset(file2, county == county_var)
colnames(file2) <- c("X", "year", "damagecause", "county", "state", "commodity", predictor_var )
climatevar$zscore <- scale(climatevar[1], center = TRUE, scale = TRUE)
colnames(climatevar[3]) <- "zscore"
kor <- join(climatevar, file2, by = "year")
kor2 <- subset(kor, damagecause != "NA")
colnames(kor2)[3] <- "zscore"
# kor2[9] <- as.numeric(kor2$zscore)
kor3 <- cor(kor2[1], kor2[9])
#insert kor3 into designmatrix iteratively
dmvector[cl,] <- kor3
}
dmvector <- as.data.frame(dmvector)
colnames(dmvector) <- "correlations"
dmvector2 <- (matrix(dmvector$correlations, 12, 12, TRUE) )
dmvector2 <- dmvector2[nrow(dmvector2):1, ]
dmvector3 <- dmvector2[4:12,]
dmvector3[9,4:12] <- NA
dmvector3[8,5:12] <- NA
dmvector3[7,6:12] <- NA
dmvector3[6,7:12] <- NA
dmvector3[5,8:12] <- NA
dmvector3[4,9:12] <- NA
dmvector3[3,10:12] <- NA
dmvector3[2,11:12] <- NA
dmvector3[1,12:12] <- NA
dmvector <- c(dmvector3[9,], dmvector3[8,], dmvector3[7,], dmvector3[6,], dmvector3[5,], dmvector3[4,], dmvector3[3,], dmvector3[2,], dmvector3[1,])
dmvector <- data.frame(dmvector)
if(climate_var=='pr'){
dmv <- which.min( dmvector[1:108,1])
} else {
if(climate_var=='rmin'){
dmv <- which.min( dmvector[1:108,1])
} else {
if(climate_var=='rmax'){
dmv <- which.min( dmvector[1:108,1])
} else {
if(climate_var=='tmmn'){
dmv <- which.max( dmvector[1:108,1])
} else {
if(climate_var=='pet'){
dmv <- which.max( dmvector[1:108,1])
} else {
if(climate_var=='fm100'){
dmv <- which.min( dmvector[1:108,1])
} else {
if(climate_var=='fm1000'){
dmv <- which.min( dmvector[1:108,1])
} else {
if(climate_var=='pdsi'){
dmv <- which.min( dmvector[1:108,1])
} else {
if(climate_var=='tmmx'){
dmv <- which.max( dmvector[1:108,1])
}
}
}
}
}
}
}
}
}
#dmv <- which.max(abs( dmvector[1:108,1]) )
dmv <- as.data.frame(dmv)
colnames(dmv)[1] <- "row"
#dmvector1a <- max(dmvector$correlations)
#dmvector1b <- data.frame(which=dmvector, correlations=dmvector[dmvector1a, ])
monthnumlist2 <- cbind(as.data.frame(c(1:144)), monthnumlist)
colnames(monthnumlist2)[1] <- "row"
monthnumlist3 <- subset(monthnumlist2, row == dmv$row)
#makeRects <- function(tfMat){
require(utils)
cAbove <- expand.grid(1:12, 1:9)[monthnumlist3$row,]
monthzzz <- c("Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep")
my_palette <- colorRampPalette(c("red", "yellow", "green"))(n = 108)
setwd("/dmine/data/USDA/agmesh-scenarios/Allstates/climatematrix_pngs")
# png(filename=paste(state1, "_", county1, "_", damage1, "_", climate_variable12, "_designmatrix.png", sep=""))
lblcol <- c(9:1)
dmvector3a <- round(dmvector3, digits = 2)
newone <<- as.data.frame(expand.grid(1:12, 1:9)[monthnumlist3$row,])
dmvector3a[9,4:12] <- NA
dmvector3a[8,5:12] <- NA
dmvector3a[7,6:12] <- NA
dmvector3a[6,7:12] <- NA
dmvector3a[5,8:12] <- NA
dmvector3a[4,9:12] <- NA
dmvector3a[3,10:12] <- NA
dmvector3a[2,11:12] <- NA
dmvector3a[1,12:12] <- NA
my_palette <- colorRampPalette(c("lightyellow", "yellow", "orange", "darkorange", "red", "darkred"))(n = 32)
nba_heatmap <- heatmap.2(dmvector3, Rowv=NA, Colv=NA, cexCol = 2, cexRow = 2, colsep=1:ncol(dmvector3), rowsep=1:nrow(dmvector3), col=my_palette, symbreaks = FALSE, scale="none", dendrogram = "none", trace="none", labRow = rev(monthzzz), tracecol = "black", key = TRUE, notecol = "black", notecex = 1.5, key.ylab = "", key.xlab = "loss correlation range", key.title = "", main=paste(climate_var, " vs ", predictor_var, "\n", county_var, " County ", state_var, " ", damage_var, "\n", "MAX Correlation in cell: ", monthnumlist3$combined, sep=""))
# nba_heatmap <- heatmap.2(dmvector3, Rowv=NA, Colv=NA, col=colorRampPalette(c("lightyellow", "yellow", "orange", "darkorange", "red")), scale="none", dendrogram = "none", trace="none", labRow = rev(monthzzz), tracecol = "black", key = TRUE, cellnote = dmvector3a, notecol = "black", notecex = 1.5, key.ylab = "", key.xlab = "loss correlation range", key.title = "", main=paste(climate_var, " vs ", predictor_var, "\n", county_var, " County ", state_var, " ", damage_var, "\n", "MAX Correlation in cell: ", monthnumlist3$combined, sep=""), add.expr=points(newone$Var1,newone$Var2, pch=15, cex = 5.5 , col=rgb(199/255, 0, 0, 0.8)))Step 2.3. Precipitation design matrix for Whitman County, WA, wheat insurance loss due to drought
state_var <- "WA"
county_var <- "Whitman"
commodity_var <- "WHEAT"
damage_var <- "Drought"
climate_var <- "pr"
predictor_var <- "cube_root_loss"
library(gplots)
library(plyr)
library(dplyr)
#library(plotly)
#packageVersion('plotly')
#response may be: loss_month, loss_per_acres, loss_year, total_acres_harvested, and total_acres_loss
#monthlist is jan-dec, each repeated 12 times
monthlist <- as.data.frame(rep(tolower(month.abb), each = 12))
#numlist is 12 months, repeated 12 times
numlist <- as.data.frame(rep((1:12), times = 12))
#monthnumlist puts month names and 1-12 together, which results in a vector of 144 that is the matrix of 12 by 12
monthnumlist <- as.data.frame(cbind(monthlist, numlist))
#renaming columns
colnames(monthnumlist) <- c("month", "monthcode")
#put the monthlist all together in one vector
monthnumlist$combined <- paste(monthnumlist$month, monthnumlist$monthcode, sep="")
#rename the vector
climmonth <- monthnumlist$combined
designmatrix <- matrix(NA, ncol=12, nrow=12)
#create an empty 144 vector to fill up with correlations between loss and climate
dmvector <- as.data.frame(rep(NA, times=144))
cl = 0
for (ppp in climmonth) {
cl = cl +1
setwd("/dmine/data/USDA/agmesh-scenarios/Allstates/climatematrix_rev4")
file1 <- read.csv(paste(state_var, "_", county_var, "_", commodity_var, "_", damage_var, "_", ppp, ".csv", sep=""))
climatevar <- as.data.frame(cbind((file1[climate_var]), file1[2]))
setwd("/dmine/data/USDA/agmesh-scenarios/Allstates/climatematrix_summaries_rev3")
file2 <- as.data.frame(read.csv(paste(state_var, "_", county_var, "_", commodity_var, "_", damage_var, "_", predictor_var, ".csv", sep="")))
file2 <- subset(file2, state == state_var)
file2 <- subset(file2, county == county_var)
colnames(file2) <- c("X", "year", "damagecause", "county", "state", "commodity", predictor_var )
climatevar$zscore <- scale(climatevar[1], center = TRUE, scale = TRUE)
colnames(climatevar[3]) <- "zscore"
kor <- join(climatevar, file2, by = "year")
kor2 <- subset(kor, damagecause != "NA")
colnames(kor2)[3] <- "zscore"
# kor2[9] <- as.numeric(kor2$zscore)
kor3 <- cor(kor2[1], kor2[9])
#insert kor3 into designmatrix iteratively
dmvector[cl,] <- kor3
}
dmvector <- as.data.frame(dmvector)
colnames(dmvector) <- "correlations"
dmvector2 <- (matrix(dmvector$correlations, 12, 12, TRUE) )
dmvector2 <- dmvector2[nrow(dmvector2):1, ]
dmvector3 <- dmvector2[4:12,]
dmvector3[9,4:12] <- NA
dmvector3[8,5:12] <- NA
dmvector3[7,6:12] <- NA
dmvector3[6,7:12] <- NA
dmvector3[5,8:12] <- NA
dmvector3[4,9:12] <- NA
dmvector3[3,10:12] <- NA
dmvector3[2,11:12] <- NA
dmvector3[1,12:12] <- NA
dmvector <- c(dmvector3[9,], dmvector3[8,], dmvector3[7,], dmvector3[6,], dmvector3[5,], dmvector3[4,], dmvector3[3,], dmvector3[2,], dmvector3[1,])
dmvector <- data.frame(dmvector)
if(climate_var=='pr'){
dmv <- which.min( dmvector[1:108,1])
} else {
if(climate_var=='rmin'){
dmv <- which.min( dmvector[1:108,1])
} else {
if(climate_var=='rmax'){
dmv <- which.min( dmvector[1:108,1])
} else {
if(climate_var=='tmmn'){
dmv <- which.max( dmvector[1:108,1])
} else {
if(climate_var=='pet'){
dmv <- which.max( dmvector[1:108,1])
} else {
if(climate_var=='fm100'){
dmv <- which.min( dmvector[1:108,1])
} else {
if(climate_var=='fm1000'){
dmv <- which.min( dmvector[1:108,1])
} else {
if(climate_var=='pdsi'){
dmv <- which.min( dmvector[1:108,1])
} else {
if(climate_var=='tmmx'){
dmv <- which.max( dmvector[1:108,1])
}
}
}
}
}
}
}
}
}
#dmv <- which.max(abs( dmvector[1:108,1]) )
dmv <- as.data.frame(dmv)
colnames(dmv)[1] <- "row"
#dmvector1a <- max(dmvector$correlations)
#dmvector1b <- data.frame(which=dmvector, correlations=dmvector[dmvector1a, ])
monthnumlist2 <- cbind(as.data.frame(c(1:144)), monthnumlist)
colnames(monthnumlist2)[1] <- "row"
monthnumlist3 <- subset(monthnumlist2, row == dmv$row)
#makeRects <- function(tfMat){
require(utils)
cAbove <- expand.grid(1:12, 1:9)[monthnumlist3$row,]
monthzzz <- c("Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep")
my_palette <- colorRampPalette(c("red", "yellow", "green"))(n = 108)
setwd("/dmine/data/USDA/agmesh-scenarios/Allstates/climatematrix_pngs")
# png(filename=paste(state1, "_", county1, "_", damage1, "_", climate_variable12, "_designmatrix.png", sep=""))
lblcol <- c(9:1)
dmvector3a <- round(dmvector3, digits = 2)
newone <<- as.data.frame(expand.grid(1:12, 1:9)[monthnumlist3$row,])
dmvector3a[9,4:12] <- NA
dmvector3a[8,5:12] <- NA
dmvector3a[7,6:12] <- NA
dmvector3a[6,7:12] <- NA
dmvector3a[5,8:12] <- NA
dmvector3a[4,9:12] <- NA
dmvector3a[3,10:12] <- NA
dmvector3a[2,11:12] <- NA
dmvector3a[1,12:12] <- NA
my_palette <- colorRampPalette(c("darkred", "red", "darkorange", "orange", "yellow", "lightyellow"))(n = 16)
nba_heatmap <- heatmap.2(dmvector3, Rowv=NA, Colv=NA, cexCol = 2, cexRow = 2, colsep=1:ncol(dmvector3), rowsep=1:nrow(dmvector3), col=my_palette, scale="none", dendrogram = "none", trace="none", labRow = rev(monthzzz), tracecol = "black", key = TRUE, notecol = "black", notecex = 1.5, key.ylab = "", key.xlab = "loss correlation range", key.title = "", main=paste(climate_var, " vs ", predictor_var, "\n", county_var, " County ", state_var, " ", damage_var, "\n", "MAX Correlation in cell: ", monthnumlist3$combined, sep=""))
# nba_heatmap <- heatmap.2(dmvector3, Rowv=NA, Colv=NA, col=topo.colors(16), scale="none", dendrogram = "none", trace="none", labRow = rev(monthzzz), tracecol = "black", key = TRUE, cellnote = dmvector3a, notecol = "black", notecex = 1.5, key.ylab = "", key.xlab = "loss correlation range", key.title = "", main=paste(climate_var, " vs ", predictor_var, "\n", county_var, " County ", state_var, " ", damage_var, "\n", "MAX Correlation in cell: ", monthnumlist3$combined, sep=""), add.expr=points(newone$Var1,newone$Var2, pch=15, cex = 5.5 , col=rgb(199/255, 0, 0, 0.8)))Step 2.3. Palmer Drought Severity Index (PDSI) design matrix for Whitman County, WA, wheat insurance loss due to drought
state_var <- "WA"
county_var <- "Whitman"
commodity_var <- "WHEAT"
damage_var <- "Drought"
climate_var <- "pdsi"
predictor_var <- "cube_root_loss"
library(gplots)
library(plyr)
library(dplyr)
#library(plotly)
#packageVersion('plotly')
#response may be: loss_month, loss_per_acres, loss_year, total_acres_harvested, and total_acres_loss
#monthlist is jan-dec, each repeated 12 times
monthlist <- as.data.frame(rep(tolower(month.abb), each = 12))
#numlist is 12 months, repeated 12 times
numlist <- as.data.frame(rep((1:12), times = 12))
#monthnumlist puts month names and 1-12 together, which results in a vector of 144 that is the matrix of 12 by 12
monthnumlist <- as.data.frame(cbind(monthlist, numlist))
#renaming columns
colnames(monthnumlist) <- c("month", "monthcode")
#put the monthlist all together in one vector
monthnumlist$combined <- paste(monthnumlist$month, monthnumlist$monthcode, sep="")
#rename the vector
climmonth <- monthnumlist$combined
designmatrix <- matrix(NA, ncol=12, nrow=12)
#create an empty 144 vector to fill up with correlations between loss and climate
dmvector <- as.data.frame(rep(NA, times=144))
cl = 0
for (ppp in climmonth) {
cl = cl +1
setwd("/dmine/data/USDA/agmesh-scenarios/Allstates/climatematrix_rev4")
file1 <- read.csv(paste(state_var, "_", county_var, "_", commodity_var, "_", damage_var, "_", ppp, ".csv", sep=""))
climatevar <- as.data.frame(cbind((file1[climate_var]), file1[2]))
setwd("/dmine/data/USDA/agmesh-scenarios/Allstates/climatematrix_summaries_rev3")
file2 <- as.data.frame(read.csv(paste(state_var, "_", county_var, "_", commodity_var, "_", damage_var, "_", predictor_var, ".csv", sep="")))
file2 <- subset(file2, state == state_var)
file2 <- subset(file2, county == county_var)
colnames(file2) <- c("X", "year", "damagecause", "county", "state", "commodity", predictor_var )
climatevar$zscore <- scale(climatevar[1], center = TRUE, scale = TRUE)
colnames(climatevar[3]) <- "zscore"
kor <- join(climatevar, file2, by = "year")
kor2 <- subset(kor, damagecause != "NA")
colnames(kor2)[3] <- "zscore"
# kor2[9] <- as.numeric(kor2$zscore)
kor3 <- cor(kor2[1], kor2[9])
#insert kor3 into designmatrix iteratively
dmvector[cl,] <- kor3
}
dmvector <- as.data.frame(dmvector)
colnames(dmvector) <- "correlations"
dmvector2 <- (matrix(dmvector$correlations, 12, 12, TRUE) )
dmvector2 <- dmvector2[nrow(dmvector2):1, ]
dmvector3 <- dmvector2[4:12,]
dmvector3[9,4:12] <- NA
dmvector3[8,5:12] <- NA
dmvector3[7,6:12] <- NA
dmvector3[6,7:12] <- NA
dmvector3[5,8:12] <- NA
dmvector3[4,9:12] <- NA
dmvector3[3,10:12] <- NA
dmvector3[2,11:12] <- NA
dmvector3[1,12:12] <- NA
dmvector <- c(dmvector3[9,], dmvector3[8,], dmvector3[7,], dmvector3[6,], dmvector3[5,], dmvector3[4,], dmvector3[3,], dmvector3[2,], dmvector3[1,])
dmvector <- data.frame(dmvector)
if(climate_var=='pr'){
dmv <- which.min( dmvector[1:108,1])
} else {
if(climate_var=='rmin'){
dmv <- which.min( dmvector[1:108,1])
} else {
if(climate_var=='rmax'){
dmv <- which.min( dmvector[1:108,1])
} else {
if(climate_var=='tmmn'){
dmv <- which.max( dmvector[1:108,1])
} else {
if(climate_var=='pet'){
dmv <- which.max( dmvector[1:108,1])
} else {
if(climate_var=='fm100'){
dmv <- which.min( dmvector[1:108,1])
} else {
if(climate_var=='fm1000'){
dmv <- which.min( dmvector[1:108,1])
} else {
if(climate_var=='pdsi'){
dmv <- which.min( dmvector[1:108,1])
} else {
if(climate_var=='tmmx'){
dmv <- which.max( dmvector[1:108,1])
}
}
}
}
}
}
}
}
}
#dmv <- which.max(abs( dmvector[1:108,1]) )
dmv <- as.data.frame(dmv)
colnames(dmv)[1] <- "row"
#dmvector1a <- max(dmvector$correlations)
#dmvector1b <- data.frame(which=dmvector, correlations=dmvector[dmvector1a, ])
monthnumlist2 <- cbind(as.data.frame(c(1:144)), monthnumlist)
colnames(monthnumlist2)[1] <- "row"
monthnumlist3 <- subset(monthnumlist2, row == dmv$row)
#makeRects <- function(tfMat){
require(utils)
cAbove <- expand.grid(1:12, 1:9)[monthnumlist3$row,]
monthzzz <- c("Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep")
my_palette <- colorRampPalette(c("red", "yellow", "green"))(n = 108)
setwd("/dmine/data/USDA/agmesh-scenarios/Allstates/climatematrix_pngs")
# png(filename=paste(state1, "_", county1, "_", damage1, "_", climate_variable12, "_designmatrix.png", sep=""))
lblcol <- c(9:1)
dmvector3a <- round(dmvector3, digits = 2)
newone <<- as.data.frame(expand.grid(1:12, 1:9)[monthnumlist3$row,])
dmvector3a[9,4:12] <- NA
dmvector3a[8,5:12] <- NA
dmvector3a[7,6:12] <- NA
dmvector3a[6,7:12] <- NA
dmvector3a[5,8:12] <- NA
dmvector3a[4,9:12] <- NA
dmvector3a[3,10:12] <- NA
dmvector3a[2,11:12] <- NA
dmvector3a[1,12:12] <- NA
my_palette <- colorRampPalette(c("darkred", "red", "darkorange", "orange", "yellow", "lightyellow"))(n = 16)
nba_heatmap <- heatmap.2(dmvector3, Rowv=NA, Colv=NA, cexCol = 2, cexRow = 2, colsep=1:ncol(dmvector3), rowsep=1:nrow(dmvector3), col=my_palette, scale="none", dendrogram = "none", trace="none", labRow = rev(monthzzz), tracecol = "black", key = TRUE, notecol = "black", notecex = 1.5, key.ylab = "", key.xlab = "loss correlation range", key.title = "", main=paste(climate_var, " vs ", predictor_var, "\n", county_var, " County ", state_var, " ", damage_var, "\n", "MAX Correlation in cell: ", monthnumlist3$combined, sep=""))
# nba_heatmap <- heatmap.2(dmvector3, Rowv=NA, Colv=NA, col=topo.colors(16), scale="none", dendrogram = "none", trace="none", labRow = rev(monthzzz), tracecol = "black", key = TRUE, cellnote = dmvector3a, notecol = "black", notecex = 1.5, key.ylab = "", key.xlab = "loss correlation range", key.title = "", main=paste(climate_var, " vs ", predictor_var, "\n", county_var, " County ", state_var, " ", damage_var, "\n", "MAX Correlation in cell: ", monthnumlist3$combined, sep=""), add.expr=points(newone$Var1,newone$Var2, pch=15, cex = 5.5 , col=rgb(199/255, 0, 0, 0.8)))Step 3. Examining spatial relationships of climate lagged correlations
In step 3, we generate maps of our climate-lagged correlations, for each climate variable. Each county is labeled with the optimum monthly period that has the highest correlation for that climate variable and wheat/drought insurance loss (July 2 = two months previous to July, or May/June/July), along with the correlation value. Each correlation value is absolute (correlation coefficent - R)
Step 3.1. Precipitation time lagged correlations to wheat/drought insurance loss, per county, for the iPNW
library(maptools)
library(RColorBrewer)
library(leaflet)
library(raster)
climate_var <- "pr"
predictor_var <- "cube_root_loss"
monthend <- "jul"
monthnumber <- 2
library(RColorBrewer)
library(dplyr)
setwd("/dmine/data/USDA/agmesh-scenarios/Allstates/climatematrix_correlations_3/")
if (predictor_var == "loss") {
predictor <- "crop_commodity_loss"
}else {
predictor <- predictor_var
}
files <- list.files(pattern = predictor)
filey <- do.call(rbind, strsplit(files, '[_]'))
filey <- subset(filey, filey[,5] == climate_var)
colnames(filey) <- c("state", "county", "commodity", "damage", "climate", "crop1", "crop2", "response", "crop3")
filey <- as.data.frame(filey)
data <- with(filey, paste(state, "_", county, "_", commodity, "_", damage, "_", climate, "_", crop1, "_", crop2, "_", response, "_", crop3, sep=""))
tables <- lapply(data, read.csv, header = TRUE)
tables <- lapply(tables, function(x) { x["X"] <- NULL; x }) #--remove first index row from each list
tables <- lapply(tables, function(x) arrange(x, -row_number())) #--(flips matrix - puts jan as 1st row and sept as 9th row)## row_number() should only be called in a data context for (i in 1:26) {
tables[[i]][1,4:12] <- NA
tables[[i]][2,5:12] <- NA
tables[[i]][3,6:12] <- NA
tables[[i]][4,7:12] <- NA
tables[[i]][5,8:12] <- NA
tables[[i]][6,9:12] <- NA
tables[[i]][7,10:12] <- NA
tables[[i]][8,11:12] <- NA
tables[[i]][9,12:12] <- NA
}
monthly <- match(monthend, tolower(month.abb))
if(climate_var=='pr'){
bestcounty <- matrix(NA,nrow=26,ncol=3)
for (i in 1:26) {
temp <- which(tables[[i]] == min(tables[[i]], na.rm=T), arr.ind = TRUE)
temp2 <- min(tables[[i]], na.rm=T)
bestcounty[i,1] <- temp[1,1]
bestcounty[i,2] <- temp[1,2]
bestcounty[i,3] <- temp2
temp <- NA
temp2 <- NA
}
} else {
if(climate_var=='rmin'){
bestcounty <- matrix(NA,nrow=26,ncol=3)
for (i in 1:26) {
temp <- which(tables[[i]] == min(tables[[i]], na.rm=T), arr.ind = TRUE)
temp2 <- min(tables[[i]], na.rm=T)
bestcounty[i,1] <- temp[1,1]
bestcounty[i,2] <- temp[1,2]
bestcounty[i,3] <- temp2
temp <- NA
temp2 <- NA
}
} else {
if(climate_var=='rmax'){
bestcounty <- matrix(NA,nrow=26,ncol=3)
for (i in 1:26) {
temp <- which(tables[[i]] == max(tables[[i]], na.rm=T), arr.ind = TRUE)
temp2 <- max(tables[[i]], na.rm=T)
bestcounty[i,1] <- temp[1,1]
bestcounty[i,2] <- temp[1,2]
bestcounty[i,3] <- temp2
temp <- NA
temp2 <- NA
}
} else {
if(climate_var=='tmmx'){
bestcounty <- matrix(NA,nrow=26,ncol=3)
for (i in 1:26) {
temp <- which(tables[[i]] == max(tables[[i]], na.rm=T), arr.ind = TRUE)
temp2 <- max(tables[[i]], na.rm=T)
bestcounty[i,1] <- temp[1,1]
bestcounty[i,2] <- temp[1,2]
bestcounty[i,3] <- temp2
temp <- NA
temp2 <- NA
}
} else {
if(climate_var=='tmin'){
bestcounty <- matrix(NA,nrow=26,ncol=3)
for (i in 1:26) {
temp <- which(tables[[i]] == max(tables[[i]], na.rm=T), arr.ind = TRUE)
temp2 <- max(tables[[i]], na.rm=T)
bestcounty[i,1] <- temp[1,1]
bestcounty[i,2] <- temp[1,2]
bestcounty[i,3] <- temp2
temp <- NA
temp2 <- NA
}
} else {
if(climate_var=='fm100'){
bestcounty <- matrix(NA,nrow=26,ncol=3)
for (i in 1:26) {
temp <- which(tables[[i]] == max(tables[[i]], na.rm=T), arr.ind = TRUE)
temp2 <- max(tables[[i]], na.rm=T)
bestcounty[i,1] <- temp[1,1]
bestcounty[i,2] <- temp[1,2]
bestcounty[i,3] <- temp2
temp <- NA
temp2 <- NA
}
} else {
if(climate_var=='fm1000'){
bestcounty <- matrix(NA,nrow=26,ncol=3)
for (i in 1:26) {
temp <- which(tables[[i]] == max(tables[[i]], na.rm=T), arr.ind = TRUE)
temp2 <- max(tables[[i]], na.rm=T)
bestcounty[i,1] <- temp[1,1]
bestcounty[i,2] <- temp[1,2]
bestcounty[i,3] <- temp2
temp <- NA
temp2 <- NA
}
} else {
if(climate_var=='pet'){
bestcounty <- matrix(NA,nrow=26,ncol=3)
for (i in 1:26) {
temp <- which(tables[[i]] == max(tables[[i]], na.rm=T), arr.ind = TRUE)
temp2 <- max(tables[[i]], na.rm=T)
bestcounty[i,1] <- temp[1,1]
bestcounty[i,2] <- temp[1,2]
bestcounty[i,3] <- temp2
temp <- NA
temp2 <- NA
}
} else {
if(climate_var=='pdsi'){
bestcounty <- matrix(NA,nrow=26,ncol=3)
for (i in 1:26) {
temp <- which(tables[[i]] == min(tables[[i]], na.rm=T), arr.ind = TRUE)
temp2 <- min(tables[[i]], na.rm=T)
bestcounty[i,1] <- temp[1,1]
bestcounty[i,2] <- temp[1,2]
bestcounty[i,3] <- temp2
temp <- NA
temp2 <- NA
}
}
}
}
}
}
}
}
}
}
bestcounty[,1] <- tolower(month.abb[bestcounty[,1]])
bestcounty2 <- cbind(data.frame(filey$county), bestcounty)
colnames(bestcounty2) <- c("NAME", "MONTH", "ENDMONTH", "CORRELATION")
#new
#!!!!!!fix-row by column, or number of months by ending month
table2 <- lapply(tables, function(x) x[monthly, as.numeric(monthnumber)])
table3 <- data.frame(matrix(unlist(table2), nrow=length(table2), byrow=T))
colnames(table3) <- "correlations"
#combined <- do.call(rbind , tables)
table4 <- cbind(filey, table3)
#if (predictor_var == "loss") {
#predictor_var <- "crop_commodity_loss"
#}
table5 <- table4[c(2:5,10)]
colnames(table5) <- c("NAME", "COMMODITY", "DAMAGE", "climate", "correlations")
#table5$STATE_NAME <- state.name[match(table5[,1],state.abb)]
setwd("/dmine/data/counties/")
counties <- readShapePoly('UScounties.shp',
proj4string=CRS
("+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0"))
projection = CRS("+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0")
statez = c("Idaho", "Washington", "Oregon")
Idaho_list1 <- paste("Idaho", "Lewis", "Nez Perce", "Latah", "Benewah", sep="|")
Washington_list1 <- paste("Okananogan", "Douglas", "Grant", "Benton", "Franklin", "Walla Walla", "Adams", "Lincoln", "Spokane", "Whitman", "Columbia", "Garfield", "Asotin", sep="|")
Oregon_list1 <- paste("Wasco", "Sherman", "Gilliam", "Morrow", "Umatilla", "Union", "Wallowa", sep="|")
combinedlist2 <- paste("Okananogan", "Douglas", "Grant", "Benton", "Franklin", "Walla Walla", "Adams", "Lincoln", "Spokane", "Whitman", "Columbia", "Garfield", "Asotin", "Wasco", "Sherman", "Gilliam", "Morrow", "Umatilla", "Union", "Wallowa", "Idaho", "Lewis", "Nez Perce", "Clearwater", "Latah", "Benewah", "Kootenai", sep="|")
combinedlist <- c(Idaho_list1, Washington_list1, Oregon_list1)
#alllist <- c("Idaho", "Oregon", "Washington")
#--Oregon
setwd("/dmine/data/counties/")
counties <- readShapePoly('UScounties.shp',
proj4string=CRS
("+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0"))
projection = CRS("+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0")
or_counties <- counties[grep("Oregon", counties@data$STATE_NAME),]
palouse_Oregon_counties <- or_counties[grep(Oregon_list1, or_counties@data$NAME),]
kk="Oregon"
#counties <- counties[grep("Idaho|Washington|Oregon|Montana", counties@data$STATE_NAME),]
OR_counties <- assign(paste("palouse_", kk, "_counties", sep=""), palouse_Oregon_counties)
#counties <- counties[grep(scen_state, counties@data$STATE_NAME),]
#--loop list for county by fip
#countyfiploop <- counties@data$FIPS
#--data frame of county fip list
#countyfiplist <- data.frame(counties@data$FIPS)
#--data frame of county names
#countynames <- data.frame(counties@data$NAME)
#combo of county names and fip for this list
#countylist <- cbind(countynames, countyfiplist)
#--number of rows in county list
#countylistrows <- 12 * nrow(countylist)
#---Washington
setwd("/dmine/data/counties/")
counties <- readShapePoly('UScounties.shp',
proj4string=CRS
("+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0"))
projection = CRS("+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0")
wa_counties <- counties[grep("Washington", counties@data$STATE_NAME),]
palouse_Washington_counties <- wa_counties[grep(Washington_list1, wa_counties@data$NAME),]
kk="Washington"
#counties <- counties[grep("Idaho|Washington|Oregon|Montana", counties@data$STATE_NAME),]
WA_counties <- assign(paste("palouse_", kk, "_counties", sep=""), palouse_Washington_counties)
#counties <- counties[grep(scen_state, counties@data$STATE_NAME),]
#-----Idaho
setwd("/dmine/data/counties/")
counties <- readShapePoly('UScounties.shp',
proj4string=CRS
("+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0"))
projection = CRS("+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0")
id_counties <- counties[grep("Idaho", counties@data$STATE_NAME),]
palouse_Idaho_counties <- id_counties[grep(Idaho_list1, id_counties@data$NAME),]
kk="Idaho"
#counties <- counties[grep("Idaho|Washington|Oregon|Montana", counties@data$STATE_NAME),]
ID_counties <- assign(paste("palouse_", kk, "_counties", sep=""), palouse_Idaho_counties)
#counties <- counties[grep(scen_state, counties@data$STATE_NAME),]
counties <- rbind(ID_counties, WA_counties, OR_counties)
counties2 <- merge(counties, table5, by = "NAME" )
#--new
counties3 <- merge(counties2, bestcounty2, by = "NAME")
counties3$MONTHCOMBO <- paste(counties3$MONTH, counties3$ENDMONTH, sep="")
#--new
#colorbrew <- list(color = brewer.pal(26, c("green", "blue", "yellow")))
#my_palette <- colorRampPalette(c("darkred", "lightyellow"))(n = 26)
counties3$CORRELATION <- as.numeric(levels(counties3$CORRELATION))[counties3$CORRELATION]
#pal <- colorNumeric(rev(my_palette), na.color = "#ffffff", domain = eval(parse(text=paste("counties3$", "CORRELATION", sep=""))))
pal <- colorNumeric(colorRampPalette(c("red", "orange", "yellow", "lightyellow"))(11), na.color = "#ffffff",
domain = eval(parse(text=paste("counties3$", "CORRELATION", sep=""))))
pal2 <- colorNumeric(colorRampPalette(c("lightyellow", "yellow", "orange", "red"))(11), na.color = "#ffffff",
domain = eval(parse(text=paste("counties3$", "CORRELATION", sep=""))))
#--
myLabelFormat = function(..., reverse_order = FALSE){
if(reverse_order){
function(type = "numeric", cuts){
cuts <- sort(cuts, decreasing = T)
}
}else{
labelFormat(...)
}
}
#colorss = colorRampPalette(brewer.pal(11,"Spectral"))
#finalcol <- colorss(len <- length(counties3$CORRELATION))
#finalcol2 <- topo.colors(length(counties3$CORRELATION))[order(order(counties3$CORRELATION))]
#cellselect <- paste(monthend, monthnumber, sep="")
#par(mfrow=c(1,4))
#layout(matrix(c(1,1,1,2), 1, 4, byrow = TRUE))
#par(mar = c(1,1,1,1) + 0.1)
#plot(counties3, col = finalcol2, xaxs="i", yaxs="i")
##text(coordinates(counties2), labels=round(counties2$correlations, 2), cex=1.5, col = "black")
#added from ID
#corre <- round(as.numeric(as.character(counties3$CORRELATION)), 2)
#text(coordinates(counties2), labels=paste(counties3$MONTHCOMBO, "\n", corre, sep=""), cex=1.5, col = "white", font = 2)
#--
exte <- extent(counties3)
library(htmltools)
tag.map.title <- tags$style(HTML("
.leaflet-control.map-title {
transform: translate(-50%,20%);
position: fixed !important;
left: 50%;
text-align: center;
padding-left: 10px;
padding-right: 10px;
background: rgba(255,255,255,0.75);
font-weight: bold;
font-size: 24px;
}
"))
title <- tags$div(
tag.map.title, HTML(paste("IPNW Correlation, Climate vs. ", predictor_var, " by County for ", climate_var, sep=""))
)
lat_long <- coordinates(counties3)
#labels <- paste(counties3$MONTHCOMBO, as.character(round(counties3$CORRELATION, 2)), sep = "<br/>")
#counties3$CORRELATION <- as.numeric(levels(counties3$CORRELATION))[counties3$CORRELATION]
counties3a <- data.frame(counties3)
labs <- lapply(seq(nrow(counties3a)), function(i) {
paste0(as.character(round(counties3a[i, "CORRELATION"],2)), '<br/>',
counties3a[i, "MONTHCOMBO"])
})
map <- leaflet(data = counties3) %>%
addProviderTiles(providers$Hydda.Base) %>%
addProviderTiles(providers$Stamen.TonerLines) %>%
#addControl(title, position = "topleft", className="map-title") %>%
fitBounds(exte[1], exte[3], exte[2], exte[4]) %>% addPolygons(fillColor = ~pal(counties3$CORRELATION), fillOpacity = .8, weight = 2, stroke = TRUE, smoothFactor = 0.5, color = "black") %>%
addLabelOnlyMarkers(data = counties3, lng = lat_long[,1], lat = lat_long[,2], label = lapply(labs, HTML), labelOptions = labelOptions(noHide = TRUE, direction = 'middle', textOnly = TRUE, textsize = "14px", col = "white", style=list(
'font-family'= 'serif',
'font-style'= 'bold',
'font-size' = '14px'
))) %>%
addLegend(pal = pal2, values = counties3$CORRELATION, labels = c("1", "2"), opacity = .5, title = paste("Correlation", " Matrix", sep="<br>"),
position = "bottomright", labFormat = labelFormat(transform = function(x) sort(x, decreasing = TRUE)))
mapStep 3.2. Potential Evapotranspiration time lagged correlations to wheat/drought insurance loss, per county, for the iPNW
climate_var <- "pet"
predictor_var <- "cube_root_loss"
monthend <- "jul"
monthnumber <- 2
library(RColorBrewer)
library(dplyr)
setwd("/dmine/data/USDA/agmesh-scenarios/Allstates/climatematrix_correlations_3/")
if (predictor_var == "loss") {
predictor <- "crop_commodity_loss"
}else {
predictor <- predictor_var
}
files <- list.files(pattern = predictor)
filey <- do.call(rbind, strsplit(files, '[_]'))
filey <- subset(filey, filey[,5] == climate_var)
colnames(filey) <- c("state", "county", "commodity", "damage", "climate", "crop1", "crop2", "response", "crop3")
filey <- as.data.frame(filey)
data <- with(filey, paste(state, "_", county, "_", commodity, "_", damage, "_", climate, "_", crop1, "_", crop2, "_", response, "_", crop3, sep=""))
tables <- lapply(data, read.csv, header = TRUE)
tables <- lapply(tables, function(x) { x["X"] <- NULL; x }) #--remove first index row from each list
tables <- lapply(tables, function(x) arrange(x, -row_number())) #--(flips matrix - puts jan as 1st row and sept as 9th row)## row_number() should only be called in a data context for (i in 1:26) {
tables[[i]][1,4:12] <- NA
tables[[i]][2,5:12] <- NA
tables[[i]][3,6:12] <- NA
tables[[i]][4,7:12] <- NA
tables[[i]][5,8:12] <- NA
tables[[i]][6,9:12] <- NA
tables[[i]][7,10:12] <- NA
tables[[i]][8,11:12] <- NA
tables[[i]][9,12:12] <- NA
}
monthly <- match(monthend, tolower(month.abb))
if(climate_var=='pr'){
bestcounty <- matrix(NA,nrow=26,ncol=3)
for (i in 1:26) {
temp <- which(tables[[i]] == min(tables[[i]], na.rm=T), arr.ind = TRUE)
temp2 <- min(tables[[i]], na.rm=T)
bestcounty[i,1] <- temp[1,1]
bestcounty[i,2] <- temp[1,2]
bestcounty[i,3] <- temp2
temp <- NA
temp2 <- NA
}
} else {
if(climate_var=='rmin'){
bestcounty <- matrix(NA,nrow=26,ncol=3)
for (i in 1:26) {
temp <- which(tables[[i]] == min(tables[[i]], na.rm=T), arr.ind = TRUE)
temp2 <- min(tables[[i]], na.rm=T)
bestcounty[i,1] <- temp[1,1]
bestcounty[i,2] <- temp[1,2]
bestcounty[i,3] <- temp2
temp <- NA
temp2 <- NA
}
} else {
if(climate_var=='rmax'){
bestcounty <- matrix(NA,nrow=26,ncol=3)
for (i in 1:26) {
temp <- which(tables[[i]] == max(tables[[i]], na.rm=T), arr.ind = TRUE)
temp2 <- max(tables[[i]], na.rm=T)
bestcounty[i,1] <- temp[1,1]
bestcounty[i,2] <- temp[1,2]
bestcounty[i,3] <- temp2
temp <- NA
temp2 <- NA
}
} else {
if(climate_var=='tmmx'){
bestcounty <- matrix(NA,nrow=26,ncol=3)
for (i in 1:26) {
temp <- which(tables[[i]] == max(tables[[i]], na.rm=T), arr.ind = TRUE)
temp2 <- max(tables[[i]], na.rm=T)
bestcounty[i,1] <- temp[1,1]
bestcounty[i,2] <- temp[1,2]
bestcounty[i,3] <- temp2
temp <- NA
temp2 <- NA
}
} else {
if(climate_var=='tmin'){
bestcounty <- matrix(NA,nrow=26,ncol=3)
for (i in 1:26) {
temp <- which(tables[[i]] == max(tables[[i]], na.rm=T), arr.ind = TRUE)
temp2 <- max(tables[[i]], na.rm=T)
bestcounty[i,1] <- temp[1,1]
bestcounty[i,2] <- temp[1,2]
bestcounty[i,3] <- temp2
temp <- NA
temp2 <- NA
}
} else {
if(climate_var=='fm100'){
bestcounty <- matrix(NA,nrow=26,ncol=3)
for (i in 1:26) {
temp <- which(tables[[i]] == max(tables[[i]], na.rm=T), arr.ind = TRUE)
temp2 <- max(tables[[i]], na.rm=T)
bestcounty[i,1] <- temp[1,1]
bestcounty[i,2] <- temp[1,2]
bestcounty[i,3] <- temp2
temp <- NA
temp2 <- NA
}
} else {
if(climate_var=='fm1000'){
bestcounty <- matrix(NA,nrow=26,ncol=3)
for (i in 1:26) {
temp <- which(tables[[i]] == max(tables[[i]], na.rm=T), arr.ind = TRUE)
temp2 <- max(tables[[i]], na.rm=T)
bestcounty[i,1] <- temp[1,1]
bestcounty[i,2] <- temp[1,2]
bestcounty[i,3] <- temp2
temp <- NA
temp2 <- NA
}
} else {
if(climate_var=='pet'){
bestcounty <- matrix(NA,nrow=26,ncol=3)
for (i in 1:26) {
temp <- which(tables[[i]] == max(tables[[i]], na.rm=T), arr.ind = TRUE)
temp2 <- max(tables[[i]], na.rm=T)
bestcounty[i,1] <- temp[1,1]
bestcounty[i,2] <- temp[1,2]
bestcounty[i,3] <- temp2
temp <- NA
temp2 <- NA
}
} else {
if(climate_var=='pdsi'){
bestcounty <- matrix(NA,nrow=26,ncol=3)
for (i in 1:26) {
temp <- which(tables[[i]] == min(tables[[i]], na.rm=T), arr.ind = TRUE)
temp2 <- min(tables[[i]], na.rm=T)
bestcounty[i,1] <- temp[1,1]
bestcounty[i,2] <- temp[1,2]
bestcounty[i,3] <- temp2
temp <- NA
temp2 <- NA
}
}
}
}
}
}
}
}
}
}
bestcounty[,1] <- tolower(month.abb[bestcounty[,1]])
bestcounty2 <- cbind(data.frame(filey$county), bestcounty)
colnames(bestcounty2) <- c("NAME", "MONTH", "ENDMONTH", "CORRELATION")
#new
#!!!!!!fix-row by column, or number of months by ending month
table2 <- lapply(tables, function(x) x[monthly, as.numeric(monthnumber)])
table3 <- data.frame(matrix(unlist(table2), nrow=length(table2), byrow=T))
colnames(table3) <- "correlations"
#combined <- do.call(rbind , tables)
table4 <- cbind(filey, table3)
#if (predictor_var == "loss") {
#predictor_var <- "crop_commodity_loss"
#}
table5 <- table4[c(2:5,10)]
colnames(table5) <- c("NAME", "COMMODITY", "DAMAGE", "climate", "correlations")
#table5$STATE_NAME <- state.name[match(table5[,1],state.abb)]
setwd("/dmine/data/counties/")
counties <- readShapePoly('UScounties.shp',
proj4string=CRS
("+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0"))
projection = CRS("+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0")
statez = c("Idaho", "Washington", "Oregon")
Idaho_list1 <- paste("Idaho", "Lewis", "Nez Perce", "Latah", "Benewah", sep="|")
Washington_list1 <- paste("Okananogan", "Douglas", "Grant", "Benton", "Franklin", "Walla Walla", "Adams", "Lincoln", "Spokane", "Whitman", "Columbia", "Garfield", "Asotin", sep="|")
Oregon_list1 <- paste("Wasco", "Sherman", "Gilliam", "Morrow", "Umatilla", "Union", "Wallowa", sep="|")
combinedlist2 <- paste("Okananogan", "Douglas", "Grant", "Benton", "Franklin", "Walla Walla", "Adams", "Lincoln", "Spokane", "Whitman", "Columbia", "Garfield", "Asotin", "Wasco", "Sherman", "Gilliam", "Morrow", "Umatilla", "Union", "Wallowa", "Idaho", "Lewis", "Nez Perce", "Clearwater", "Latah", "Benewah", "Kootenai", sep="|")
combinedlist <- c(Idaho_list1, Washington_list1, Oregon_list1)
#alllist <- c("Idaho", "Oregon", "Washington")
#--Oregon
setwd("/dmine/data/counties/")
counties <- readShapePoly('UScounties.shp',
proj4string=CRS
("+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0"))
projection = CRS("+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0")
or_counties <- counties[grep("Oregon", counties@data$STATE_NAME),]
palouse_Oregon_counties <- or_counties[grep(Oregon_list1, or_counties@data$NAME),]
kk="Oregon"
#counties <- counties[grep("Idaho|Washington|Oregon|Montana", counties@data$STATE_NAME),]
OR_counties <- assign(paste("palouse_", kk, "_counties", sep=""), palouse_Oregon_counties)
#counties <- counties[grep(scen_state, counties@data$STATE_NAME),]
#--loop list for county by fip
#countyfiploop <- counties@data$FIPS
#--data frame of county fip list
#countyfiplist <- data.frame(counties@data$FIPS)
#--data frame of county names
#countynames <- data.frame(counties@data$NAME)
#combo of county names and fip for this list
#countylist <- cbind(countynames, countyfiplist)
#--number of rows in county list
#countylistrows <- 12 * nrow(countylist)
#---Washington
setwd("/dmine/data/counties/")
counties <- readShapePoly('UScounties.shp',
proj4string=CRS
("+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0"))
projection = CRS("+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0")
wa_counties <- counties[grep("Washington", counties@data$STATE_NAME),]
palouse_Washington_counties <- wa_counties[grep(Washington_list1, wa_counties@data$NAME),]
kk="Washington"
#counties <- counties[grep("Idaho|Washington|Oregon|Montana", counties@data$STATE_NAME),]
WA_counties <- assign(paste("palouse_", kk, "_counties", sep=""), palouse_Washington_counties)
#counties <- counties[grep(scen_state, counties@data$STATE_NAME),]
#-----Idaho
setwd("/dmine/data/counties/")
counties <- readShapePoly('UScounties.shp',
proj4string=CRS
("+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0"))
projection = CRS("+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0")
id_counties <- counties[grep("Idaho", counties@data$STATE_NAME),]
palouse_Idaho_counties <- id_counties[grep(Idaho_list1, id_counties@data$NAME),]
kk="Idaho"
#counties <- counties[grep("Idaho|Washington|Oregon|Montana", counties@data$STATE_NAME),]
ID_counties <- assign(paste("palouse_", kk, "_counties", sep=""), palouse_Idaho_counties)
#counties <- counties[grep(scen_state, counties@data$STATE_NAME),]
counties <- rbind(ID_counties, WA_counties, OR_counties)
counties2 <- merge(counties, table5, by = "NAME" )
#--new
counties3 <- merge(counties2, bestcounty2, by = "NAME")
counties3$MONTHCOMBO <- paste(counties3$MONTH, counties3$ENDMONTH, sep="")
#--new
#colorbrew <- list(color = brewer.pal(26, c("green", "blue", "yellow")))
#my_palette <- colorRampPalette(c("darkred", "lightyellow"))(n = 26)
counties3$CORRELATION <- as.numeric(levels(counties3$CORRELATION))[counties3$CORRELATION]
#pal <- colorNumeric(rev(my_palette), na.color = "#ffffff", domain = eval(parse(text=paste("counties3$", "CORRELATION", sep=""))))
pal <- colorNumeric(colorRampPalette(c("lightyellow", "yellow", "orange", "red"))(11), na.color = "#ffffff",
domain = eval(parse(text=paste("counties3$", "CORRELATION", sep=""))))
#--
#colorss = colorRampPalette(brewer.pal(11,"Spectral"))
#finalcol <- colorss(len <- length(counties3$CORRELATION))
#finalcol2 <- topo.colors(length(counties3$CORRELATION))[order(order(counties3$CORRELATION))]
#cellselect <- paste(monthend, monthnumber, sep="")
#par(mfrow=c(1,4))
#layout(matrix(c(1,1,1,2), 1, 4, byrow = TRUE))
#par(mar = c(1,1,1,1) + 0.1)
#plot(counties3, col = finalcol2, xaxs="i", yaxs="i")
##text(coordinates(counties2), labels=round(counties2$correlations, 2), cex=1.5, col = "black")
#added from ID
#corre <- round(as.numeric(as.character(counties3$CORRELATION)), 2)
#text(coordinates(counties2), labels=paste(counties3$MONTHCOMBO, "\n", corre, sep=""), cex=1.5, col = "white", font = 2)
#--
exte <- extent(counties3)
library(htmltools)
tag.map.title <- tags$style(HTML("
.leaflet-control.map-title {
transform: translate(-50%,20%);
position: fixed !important;
left: 50%;
text-align: center;
padding-left: 10px;
padding-right: 10px;
background: rgba(255,255,255,0.75);
font-weight: bold;
font-size: 24px;
}
"))
title <- tags$div(
tag.map.title, HTML(paste("IPNW Correlation, Climate vs. ", predictor_var, " by County for ", climate_var, sep=""))
)
lat_long <- coordinates(counties3)
#labels <- paste(counties3$MONTHCOMBO, as.character(round(counties3$CORRELATION, 2)), sep = "<br/>")
#counties3$CORRELATION <- as.numeric(levels(counties3$CORRELATION))[counties3$CORRELATION]
counties3a <- data.frame(counties3)
labs <- lapply(seq(nrow(counties3a)), function(i) {
paste0(as.character(round(counties3a[i, "CORRELATION"],2)), '<br/>',
counties3a[i, "MONTHCOMBO"])
})
map <- leaflet(data = counties3) %>%
addProviderTiles(providers$Hydda.Base) %>%
addProviderTiles(providers$Stamen.TonerLines) %>%
#addControl(title, position = "topleft", className="map-title") %>%
fitBounds(exte[1], exte[3], exte[2], exte[4]) %>% addPolygons(fillColor = ~pal(counties3$CORRELATION), fillOpacity = .8, weight = 2, stroke = TRUE, smoothFactor = 0.5, color = "black") %>%
addLabelOnlyMarkers(data = counties3, lng = lat_long[,1], lat = lat_long[,2], label = lapply(labs, HTML), labelOptions = labelOptions(noHide = TRUE, direction = 'middle', textOnly = TRUE, textsize = "14px", col = "white", style=list(
'font-family'= 'serif',
'font-style'= 'bold',
'font-size' = '14px'
))) %>%
addLegend(pal = pal, values = counties3$CORRELATION, labels = c("1", "2"), opacity = .5, title = paste("Correlation", " Matrix", sep="<br>"),
position = "bottomright")
mapStep 3.1. Maximum Temperature time lagged correlations to wheat/drought insurance loss, per county, for the iPNW
climate_var <- "tmmx"
predictor_var <- "cube_root_loss"
monthend <- "jul"
monthnumber <- 2
library(RColorBrewer)
library(dplyr)
setwd("/dmine/data/USDA/agmesh-scenarios/Allstates/climatematrix_correlations_3/")
if (predictor_var == "loss") {
predictor <- "crop_commodity_loss"
}else {
predictor <- predictor_var
}
files <- list.files(pattern = predictor)
filey <- do.call(rbind, strsplit(files, '[_]'))
filey <- subset(filey, filey[,5] == climate_var)
colnames(filey) <- c("state", "county", "commodity", "damage", "climate", "crop1", "crop2", "response", "crop3")
filey <- as.data.frame(filey)
data <- with(filey, paste(state, "_", county, "_", commodity, "_", damage, "_", climate, "_", crop1, "_", crop2, "_", response, "_", crop3, sep=""))
tables <- lapply(data, read.csv, header = TRUE)
tables <- lapply(tables, function(x) { x["X"] <- NULL; x }) #--remove first index row from each list
tables <- lapply(tables, function(x) arrange(x, -row_number())) #--(flips matrix - puts jan as 1st row and sept as 9th row)## row_number() should only be called in a data context for (i in 1:26) {
tables[[i]][1,4:12] <- NA
tables[[i]][2,5:12] <- NA
tables[[i]][3,6:12] <- NA
tables[[i]][4,7:12] <- NA
tables[[i]][5,8:12] <- NA
tables[[i]][6,9:12] <- NA
tables[[i]][7,10:12] <- NA
tables[[i]][8,11:12] <- NA
tables[[i]][9,12:12] <- NA
}
monthly <- match(monthend, tolower(month.abb))
if(climate_var=='pr'){
bestcounty <- matrix(NA,nrow=26,ncol=3)
for (i in 1:26) {
temp <- which(tables[[i]] == min(tables[[i]], na.rm=T), arr.ind = TRUE)
temp2 <- min(tables[[i]], na.rm=T)
bestcounty[i,1] <- temp[1,1]
bestcounty[i,2] <- temp[1,2]
bestcounty[i,3] <- temp2
temp <- NA
temp2 <- NA
}
} else {
if(climate_var=='rmin'){
bestcounty <- matrix(NA,nrow=26,ncol=3)
for (i in 1:26) {
temp <- which(tables[[i]] == min(tables[[i]], na.rm=T), arr.ind = TRUE)
temp2 <- min(tables[[i]], na.rm=T)
bestcounty[i,1] <- temp[1,1]
bestcounty[i,2] <- temp[1,2]
bestcounty[i,3] <- temp2
temp <- NA
temp2 <- NA
}
} else {
if(climate_var=='rmax'){
bestcounty <- matrix(NA,nrow=26,ncol=3)
for (i in 1:26) {
temp <- which(tables[[i]] == max(tables[[i]], na.rm=T), arr.ind = TRUE)
temp2 <- max(tables[[i]], na.rm=T)
bestcounty[i,1] <- temp[1,1]
bestcounty[i,2] <- temp[1,2]
bestcounty[i,3] <- temp2
temp <- NA
temp2 <- NA
}
} else {
if(climate_var=='tmmx'){
bestcounty <- matrix(NA,nrow=26,ncol=3)
for (i in 1:26) {
temp <- which(tables[[i]] == max(tables[[i]], na.rm=T), arr.ind = TRUE)
temp2 <- max(tables[[i]], na.rm=T)
bestcounty[i,1] <- temp[1,1]
bestcounty[i,2] <- temp[1,2]
bestcounty[i,3] <- temp2
temp <- NA
temp2 <- NA
}
} else {
if(climate_var=='tmin'){
bestcounty <- matrix(NA,nrow=26,ncol=3)
for (i in 1:26) {
temp <- which(tables[[i]] == max(tables[[i]], na.rm=T), arr.ind = TRUE)
temp2 <- max(tables[[i]], na.rm=T)
bestcounty[i,1] <- temp[1,1]
bestcounty[i,2] <- temp[1,2]
bestcounty[i,3] <- temp2
temp <- NA
temp2 <- NA
}
} else {
if(climate_var=='fm100'){
bestcounty <- matrix(NA,nrow=26,ncol=3)
for (i in 1:26) {
temp <- which(tables[[i]] == max(tables[[i]], na.rm=T), arr.ind = TRUE)
temp2 <- max(tables[[i]], na.rm=T)
bestcounty[i,1] <- temp[1,1]
bestcounty[i,2] <- temp[1,2]
bestcounty[i,3] <- temp2
temp <- NA
temp2 <- NA
}
} else {
if(climate_var=='fm1000'){
bestcounty <- matrix(NA,nrow=26,ncol=3)
for (i in 1:26) {
temp <- which(tables[[i]] == max(tables[[i]], na.rm=T), arr.ind = TRUE)
temp2 <- max(tables[[i]], na.rm=T)
bestcounty[i,1] <- temp[1,1]
bestcounty[i,2] <- temp[1,2]
bestcounty[i,3] <- temp2
temp <- NA
temp2 <- NA
}
} else {
if(climate_var=='pet'){
bestcounty <- matrix(NA,nrow=26,ncol=3)
for (i in 1:26) {
temp <- which(tables[[i]] == max(tables[[i]], na.rm=T), arr.ind = TRUE)
temp2 <- max(tables[[i]], na.rm=T)
bestcounty[i,1] <- temp[1,1]
bestcounty[i,2] <- temp[1,2]
bestcounty[i,3] <- temp2
temp <- NA
temp2 <- NA
}
} else {
if(climate_var=='pdsi'){
bestcounty <- matrix(NA,nrow=26,ncol=3)
for (i in 1:26) {
temp <- which(tables[[i]] == min(tables[[i]], na.rm=T), arr.ind = TRUE)
temp2 <- min(tables[[i]], na.rm=T)
bestcounty[i,1] <- temp[1,1]
bestcounty[i,2] <- temp[1,2]
bestcounty[i,3] <- temp2
temp <- NA
temp2 <- NA
}
}
}
}
}
}
}
}
}
}
bestcounty[,1] <- tolower(month.abb[bestcounty[,1]])
bestcounty2 <- cbind(data.frame(filey$county), bestcounty)
colnames(bestcounty2) <- c("NAME", "MONTH", "ENDMONTH", "CORRELATION")
#new
#!!!!!!fix-row by column, or number of months by ending month
table2 <- lapply(tables, function(x) x[monthly, as.numeric(monthnumber)])
table3 <- data.frame(matrix(unlist(table2), nrow=length(table2), byrow=T))
colnames(table3) <- "correlations"
#combined <- do.call(rbind , tables)
table4 <- cbind(filey, table3)
#if (predictor_var == "loss") {
#predictor_var <- "crop_commodity_loss"
#}
table5 <- table4[c(2:5,10)]
colnames(table5) <- c("NAME", "COMMODITY", "DAMAGE", "climate", "correlations")
#table5$STATE_NAME <- state.name[match(table5[,1],state.abb)]
setwd("/dmine/data/counties/")
counties <- readShapePoly('UScounties.shp',
proj4string=CRS
("+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0"))
projection = CRS("+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0")
statez = c("Idaho", "Washington", "Oregon")
Idaho_list1 <- paste("Idaho", "Lewis", "Nez Perce", "Latah", "Benewah", sep="|")
Washington_list1 <- paste("Okananogan", "Douglas", "Grant", "Benton", "Franklin", "Walla Walla", "Adams", "Lincoln", "Spokane", "Whitman", "Columbia", "Garfield", "Asotin", sep="|")
Oregon_list1 <- paste("Wasco", "Sherman", "Gilliam", "Morrow", "Umatilla", "Union", "Wallowa", sep="|")
combinedlist2 <- paste("Okananogan", "Douglas", "Grant", "Benton", "Franklin", "Walla Walla", "Adams", "Lincoln", "Spokane", "Whitman", "Columbia", "Garfield", "Asotin", "Wasco", "Sherman", "Gilliam", "Morrow", "Umatilla", "Union", "Wallowa", "Idaho", "Lewis", "Nez Perce", "Clearwater", "Latah", "Benewah", "Kootenai", sep="|")
combinedlist <- c(Idaho_list1, Washington_list1, Oregon_list1)
#alllist <- c("Idaho", "Oregon", "Washington")
#--Oregon
setwd("/dmine/data/counties/")
counties <- readShapePoly('UScounties.shp',
proj4string=CRS
("+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0"))
projection = CRS("+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0")
or_counties <- counties[grep("Oregon", counties@data$STATE_NAME),]
palouse_Oregon_counties <- or_counties[grep(Oregon_list1, or_counties@data$NAME),]
kk="Oregon"
#counties <- counties[grep("Idaho|Washington|Oregon|Montana", counties@data$STATE_NAME),]
OR_counties <- assign(paste("palouse_", kk, "_counties", sep=""), palouse_Oregon_counties)
#counties <- counties[grep(scen_state, counties@data$STATE_NAME),]
#--loop list for county by fip
#countyfiploop <- counties@data$FIPS
#--data frame of county fip list
#countyfiplist <- data.frame(counties@data$FIPS)
#--data frame of county names
#countynames <- data.frame(counties@data$NAME)
#combo of county names and fip for this list
#countylist <- cbind(countynames, countyfiplist)
#--number of rows in county list
#countylistrows <- 12 * nrow(countylist)
#---Washington
setwd("/dmine/data/counties/")
counties <- readShapePoly('UScounties.shp',
proj4string=CRS
("+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0"))
projection = CRS("+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0")
wa_counties <- counties[grep("Washington", counties@data$STATE_NAME),]
palouse_Washington_counties <- wa_counties[grep(Washington_list1, wa_counties@data$NAME),]
kk="Washington"
#counties <- counties[grep("Idaho|Washington|Oregon|Montana", counties@data$STATE_NAME),]
WA_counties <- assign(paste("palouse_", kk, "_counties", sep=""), palouse_Washington_counties)
#counties <- counties[grep(scen_state, counties@data$STATE_NAME),]
#-----Idaho
setwd("/dmine/data/counties/")
counties <- readShapePoly('UScounties.shp',
proj4string=CRS
("+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0"))
projection = CRS("+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0")
id_counties <- counties[grep("Idaho", counties@data$STATE_NAME),]
palouse_Idaho_counties <- id_counties[grep(Idaho_list1, id_counties@data$NAME),]
kk="Idaho"
#counties <- counties[grep("Idaho|Washington|Oregon|Montana", counties@data$STATE_NAME),]
ID_counties <- assign(paste("palouse_", kk, "_counties", sep=""), palouse_Idaho_counties)
#counties <- counties[grep(scen_state, counties@data$STATE_NAME),]
counties <- rbind(ID_counties, WA_counties, OR_counties)
counties2 <- merge(counties, table5, by = "NAME" )
#--new
counties3 <- merge(counties2, bestcounty2, by = "NAME")
counties3$MONTHCOMBO <- paste(counties3$MONTH, counties3$ENDMONTH, sep="")
#--new
#colorbrew <- list(color = brewer.pal(26, c("green", "blue", "yellow")))
#my_palette <- colorRampPalette(c("darkred", "lightyellow"))(n = 26)
counties3$CORRELATION <- as.numeric(levels(counties3$CORRELATION))[counties3$CORRELATION]
#pal <- colorNumeric(rev(my_palette), na.color = "#ffffff", domain = eval(parse(text=paste("counties3$", "CORRELATION", sep=""))))
pal <- colorNumeric(colorRampPalette(c("lightyellow", "yellow", "orange", "red"))(11), na.color = "#ffffff",
domain = eval(parse(text=paste("counties3$", "CORRELATION", sep=""))))
#--
#colorss = colorRampPalette(brewer.pal(11,"Spectral"))
#finalcol <- colorss(len <- length(counties3$CORRELATION))
#finalcol2 <- topo.colors(length(counties3$CORRELATION))[order(order(counties3$CORRELATION))]
#cellselect <- paste(monthend, monthnumber, sep="")
#par(mfrow=c(1,4))
#layout(matrix(c(1,1,1,2), 1, 4, byrow = TRUE))
#par(mar = c(1,1,1,1) + 0.1)
#plot(counties3, col = finalcol2, xaxs="i", yaxs="i")
##text(coordinates(counties2), labels=round(counties2$correlations, 2), cex=1.5, col = "black")
#added from ID
#corre <- round(as.numeric(as.character(counties3$CORRELATION)), 2)
#text(coordinates(counties2), labels=paste(counties3$MONTHCOMBO, "\n", corre, sep=""), cex=1.5, col = "white", font = 2)
#--
exte <- extent(counties3)
library(htmltools)
tag.map.title <- tags$style(HTML("
.leaflet-control.map-title {
transform: translate(-50%,20%);
position: fixed !important;
left: 50%;
text-align: center;
padding-left: 10px;
padding-right: 10px;
background: rgba(255,255,255,0.75);
font-weight: bold;
font-size: 24px;
}
"))
title <- tags$div(
tag.map.title, HTML(paste("IPNW Correlation, Climate vs. ", predictor_var, " by County for ", climate_var, sep=""))
)
lat_long <- coordinates(counties3)
#labels <- paste(counties3$MONTHCOMBO, as.character(round(counties3$CORRELATION, 2)), sep = "<br/>")
#counties3$CORRELATION <- as.numeric(levels(counties3$CORRELATION))[counties3$CORRELATION]
counties3a <- data.frame(counties3)
labs <- lapply(seq(nrow(counties3a)), function(i) {
paste0(as.character(round(counties3a[i, "CORRELATION"],2)), '<br/>',
counties3a[i, "MONTHCOMBO"])
})
map <- leaflet(data = counties3) %>%
addProviderTiles(providers$Hydda.Base) %>%
addProviderTiles(providers$Stamen.TonerLines) %>%
#addControl(title, position = "topleft", className="map-title") %>%
fitBounds(exte[1], exte[3], exte[2], exte[4]) %>% addPolygons(fillColor = ~pal(counties3$CORRELATION), fillOpacity = .8, weight = 2, stroke = TRUE, smoothFactor = 0.5, color = "black") %>%
addLabelOnlyMarkers(data = counties3, lng = lat_long[,1], lat = lat_long[,2], label = lapply(labs, HTML), labelOptions = labelOptions(noHide = TRUE, direction = 'middle', textOnly = TRUE, textsize = "14px", col = "white", style=list(
'font-family'= 'serif',
'font-style'= 'bold',
'font-size' = '14px'
))) %>%
addLegend(pal = pal, values = counties3$CORRELATION, labels = c("1", "2"), opacity = .5, title = paste("Correlation", " Matrix", sep="<br>"),
position = "bottomright")
mapStep 3.1. Palmer Drought Severity Index (PDSI) time lagged correlations to wheat/drought insurance loss, per county, for the iPNW
climate_var <- "pdsi"
predictor_var <- "cube_root_loss"
monthend <- "jul"
monthnumber <- 2
library(RColorBrewer)
library(dplyr)
setwd("/dmine/data/USDA/agmesh-scenarios/Allstates/climatematrix_correlations_3/")
if (predictor_var == "loss") {
predictor <- "crop_commodity_loss"
}else {
predictor <- predictor_var
}
files <- list.files(pattern = predictor)
filey <- do.call(rbind, strsplit(files, '[_]'))
filey <- subset(filey, filey[,5] == climate_var)
colnames(filey) <- c("state", "county", "commodity", "damage", "climate", "crop1", "crop2", "response", "crop3")
filey <- as.data.frame(filey)
data <- with(filey, paste(state, "_", county, "_", commodity, "_", damage, "_", climate, "_", crop1, "_", crop2, "_", response, "_", crop3, sep=""))
tables <- lapply(data, read.csv, header = TRUE)
tables <- lapply(tables, function(x) { x["X"] <- NULL; x }) #--remove first index row from each list
tables <- lapply(tables, function(x) arrange(x, -row_number())) #--(flips matrix - puts jan as 1st row and sept as 9th row)## row_number() should only be called in a data context for (i in 1:26) {
tables[[i]][1,4:12] <- NA
tables[[i]][2,5:12] <- NA
tables[[i]][3,6:12] <- NA
tables[[i]][4,7:12] <- NA
tables[[i]][5,8:12] <- NA
tables[[i]][6,9:12] <- NA
tables[[i]][7,10:12] <- NA
tables[[i]][8,11:12] <- NA
tables[[i]][9,12:12] <- NA
}
monthly <- match(monthend, tolower(month.abb))
if(climate_var=='pr'){
bestcounty <- matrix(NA,nrow=26,ncol=3)
for (i in 1:26) {
temp <- which(tables[[i]] == min(tables[[i]], na.rm=T), arr.ind = TRUE)
temp2 <- min(tables[[i]], na.rm=T)
bestcounty[i,1] <- temp[1,1]
bestcounty[i,2] <- temp[1,2]
bestcounty[i,3] <- temp2
temp <- NA
temp2 <- NA
}
} else {
if(climate_var=='rmin'){
bestcounty <- matrix(NA,nrow=26,ncol=3)
for (i in 1:26) {
temp <- which(tables[[i]] == min(tables[[i]], na.rm=T), arr.ind = TRUE)
temp2 <- min(tables[[i]], na.rm=T)
bestcounty[i,1] <- temp[1,1]
bestcounty[i,2] <- temp[1,2]
bestcounty[i,3] <- temp2
temp <- NA
temp2 <- NA
}
} else {
if(climate_var=='rmax'){
bestcounty <- matrix(NA,nrow=26,ncol=3)
for (i in 1:26) {
temp <- which(tables[[i]] == max(tables[[i]], na.rm=T), arr.ind = TRUE)
temp2 <- max(tables[[i]], na.rm=T)
bestcounty[i,1] <- temp[1,1]
bestcounty[i,2] <- temp[1,2]
bestcounty[i,3] <- temp2
temp <- NA
temp2 <- NA
}
} else {
if(climate_var=='tmmx'){
bestcounty <- matrix(NA,nrow=26,ncol=3)
for (i in 1:26) {
temp <- which(tables[[i]] == max(tables[[i]], na.rm=T), arr.ind = TRUE)
temp2 <- max(tables[[i]], na.rm=T)
bestcounty[i,1] <- temp[1,1]
bestcounty[i,2] <- temp[1,2]
bestcounty[i,3] <- temp2
temp <- NA
temp2 <- NA
}
} else {
if(climate_var=='tmin'){
bestcounty <- matrix(NA,nrow=26,ncol=3)
for (i in 1:26) {
temp <- which(tables[[i]] == max(tables[[i]], na.rm=T), arr.ind = TRUE)
temp2 <- max(tables[[i]], na.rm=T)
bestcounty[i,1] <- temp[1,1]
bestcounty[i,2] <- temp[1,2]
bestcounty[i,3] <- temp2
temp <- NA
temp2 <- NA
}
} else {
if(climate_var=='fm100'){
bestcounty <- matrix(NA,nrow=26,ncol=3)
for (i in 1:26) {
temp <- which(tables[[i]] == max(tables[[i]], na.rm=T), arr.ind = TRUE)
temp2 <- max(tables[[i]], na.rm=T)
bestcounty[i,1] <- temp[1,1]
bestcounty[i,2] <- temp[1,2]
bestcounty[i,3] <- temp2
temp <- NA
temp2 <- NA
}
} else {
if(climate_var=='fm1000'){
bestcounty <- matrix(NA,nrow=26,ncol=3)
for (i in 1:26) {
temp <- which(tables[[i]] == max(tables[[i]], na.rm=T), arr.ind = TRUE)
temp2 <- max(tables[[i]], na.rm=T)
bestcounty[i,1] <- temp[1,1]
bestcounty[i,2] <- temp[1,2]
bestcounty[i,3] <- temp2
temp <- NA
temp2 <- NA
}
} else {
if(climate_var=='pet'){
bestcounty <- matrix(NA,nrow=26,ncol=3)
for (i in 1:26) {
temp <- which(tables[[i]] == max(tables[[i]], na.rm=T), arr.ind = TRUE)
temp2 <- max(tables[[i]], na.rm=T)
bestcounty[i,1] <- temp[1,1]
bestcounty[i,2] <- temp[1,2]
bestcounty[i,3] <- temp2
temp <- NA
temp2 <- NA
}
} else {
if(climate_var=='pdsi'){
bestcounty <- matrix(NA,nrow=26,ncol=3)
for (i in 1:26) {
temp <- which(tables[[i]] == min(tables[[i]], na.rm=T), arr.ind = TRUE)
temp2 <- min(tables[[i]], na.rm=T)
bestcounty[i,1] <- temp[1,1]
bestcounty[i,2] <- temp[1,2]
bestcounty[i,3] <- temp2
temp <- NA
temp2 <- NA
}
}
}
}
}
}
}
}
}
}
bestcounty[,1] <- tolower(month.abb[bestcounty[,1]])
bestcounty2 <- cbind(data.frame(filey$county), bestcounty)
colnames(bestcounty2) <- c("NAME", "MONTH", "ENDMONTH", "CORRELATION")
#new
#!!!!!!fix-row by column, or number of months by ending month
table2 <- lapply(tables, function(x) x[monthly, as.numeric(monthnumber)])
table3 <- data.frame(matrix(unlist(table2), nrow=length(table2), byrow=T))
colnames(table3) <- "correlations"
#combined <- do.call(rbind , tables)
table4 <- cbind(filey, table3)
#if (predictor_var == "loss") {
#predictor_var <- "crop_commodity_loss"
#}
table5 <- table4[c(2:5,10)]
colnames(table5) <- c("NAME", "COMMODITY", "DAMAGE", "climate", "correlations")
#table5$STATE_NAME <- state.name[match(table5[,1],state.abb)]
setwd("/dmine/data/counties/")
counties <- readShapePoly('UScounties.shp',
proj4string=CRS
("+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0"))
projection = CRS("+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0")
statez = c("Idaho", "Washington", "Oregon")
Idaho_list1 <- paste("Idaho", "Lewis", "Nez Perce", "Latah", "Benewah", sep="|")
Washington_list1 <- paste("Okananogan", "Douglas", "Grant", "Benton", "Franklin", "Walla Walla", "Adams", "Lincoln", "Spokane", "Whitman", "Columbia", "Garfield", "Asotin", sep="|")
Oregon_list1 <- paste("Wasco", "Sherman", "Gilliam", "Morrow", "Umatilla", "Union", "Wallowa", sep="|")
combinedlist2 <- paste("Okananogan", "Douglas", "Grant", "Benton", "Franklin", "Walla Walla", "Adams", "Lincoln", "Spokane", "Whitman", "Columbia", "Garfield", "Asotin", "Wasco", "Sherman", "Gilliam", "Morrow", "Umatilla", "Union", "Wallowa", "Idaho", "Lewis", "Nez Perce", "Clearwater", "Latah", "Benewah", "Kootenai", sep="|")
combinedlist <- c(Idaho_list1, Washington_list1, Oregon_list1)
#alllist <- c("Idaho", "Oregon", "Washington")
#--Oregon
setwd("/dmine/data/counties/")
counties <- readShapePoly('UScounties.shp',
proj4string=CRS
("+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0"))
projection = CRS("+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0")
or_counties <- counties[grep("Oregon", counties@data$STATE_NAME),]
palouse_Oregon_counties <- or_counties[grep(Oregon_list1, or_counties@data$NAME),]
kk="Oregon"
#counties <- counties[grep("Idaho|Washington|Oregon|Montana", counties@data$STATE_NAME),]
OR_counties <- assign(paste("palouse_", kk, "_counties", sep=""), palouse_Oregon_counties)
#counties <- counties[grep(scen_state, counties@data$STATE_NAME),]
#--loop list for county by fip
#countyfiploop <- counties@data$FIPS
#--data frame of county fip list
#countyfiplist <- data.frame(counties@data$FIPS)
#--data frame of county names
#countynames <- data.frame(counties@data$NAME)
#combo of county names and fip for this list
#countylist <- cbind(countynames, countyfiplist)
#--number of rows in county list
#countylistrows <- 12 * nrow(countylist)
#---Washington
setwd("/dmine/data/counties/")
counties <- readShapePoly('UScounties.shp',
proj4string=CRS
("+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0"))
projection = CRS("+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0")
wa_counties <- counties[grep("Washington", counties@data$STATE_NAME),]
palouse_Washington_counties <- wa_counties[grep(Washington_list1, wa_counties@data$NAME),]
kk="Washington"
#counties <- counties[grep("Idaho|Washington|Oregon|Montana", counties@data$STATE_NAME),]
WA_counties <- assign(paste("palouse_", kk, "_counties", sep=""), palouse_Washington_counties)
#counties <- counties[grep(scen_state, counties@data$STATE_NAME),]
#-----Idaho
setwd("/dmine/data/counties/")
counties <- readShapePoly('UScounties.shp',
proj4string=CRS
("+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0"))
projection = CRS("+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0")
id_counties <- counties[grep("Idaho", counties@data$STATE_NAME),]
palouse_Idaho_counties <- id_counties[grep(Idaho_list1, id_counties@data$NAME),]
kk="Idaho"
#counties <- counties[grep("Idaho|Washington|Oregon|Montana", counties@data$STATE_NAME),]
ID_counties <- assign(paste("palouse_", kk, "_counties", sep=""), palouse_Idaho_counties)
#counties <- counties[grep(scen_state, counties@data$STATE_NAME),]
counties <- rbind(ID_counties, WA_counties, OR_counties)
counties2 <- merge(counties, table5, by = "NAME" )
#--new
counties3 <- merge(counties2, bestcounty2, by = "NAME")
counties3$MONTHCOMBO <- paste(counties3$MONTH, counties3$ENDMONTH, sep="")
#--new
#colorbrew <- list(color = brewer.pal(26, c("green", "blue", "yellow")))
#my_palette <- colorRampPalette(c("darkred", "lightyellow"))(n = 26)
counties3$CORRELATION <- as.numeric(levels(counties3$CORRELATION))[counties3$CORRELATION]
#pal <- colorNumeric(rev(my_palette), na.color = "#ffffff", domain = eval(parse(text=paste("counties3$", "CORRELATION", sep=""))))
pal <- colorNumeric(colorRampPalette(c("lightyellow", "yellow", "orange", "red"))(11), na.color = "#ffffff",
domain = eval(parse(text=paste("counties3$", "CORRELATION", sep=""))))
#--
#colorss = colorRampPalette(brewer.pal(11,"Spectral"))
#finalcol <- colorss(len <- length(counties3$CORRELATION))
#finalcol2 <- topo.colors(length(counties3$CORRELATION))[order(order(counties3$CORRELATION))]
#cellselect <- paste(monthend, monthnumber, sep="")
#par(mfrow=c(1,4))
#layout(matrix(c(1,1,1,2), 1, 4, byrow = TRUE))
#par(mar = c(1,1,1,1) + 0.1)
#plot(counties3, col = finalcol2, xaxs="i", yaxs="i")
##text(coordinates(counties2), labels=round(counties2$correlations, 2), cex=1.5, col = "black")
#added from ID
#corre <- round(as.numeric(as.character(counties3$CORRELATION)), 2)
#text(coordinates(counties2), labels=paste(counties3$MONTHCOMBO, "\n", corre, sep=""), cex=1.5, col = "white", font = 2)
#--
exte <- extent(counties3)
library(htmltools)
tag.map.title <- tags$style(HTML("
.leaflet-control.map-title {
transform: translate(-50%,20%);
position: fixed !important;
left: 50%;
text-align: center;
padding-left: 10px;
padding-right: 10px;
background: rgba(255,255,255,0.75);
font-weight: bold;
font-size: 24px;
}
"))
title <- tags$div(
tag.map.title, HTML(paste("IPNW Correlation, Climate vs. ", predictor_var, " by County for ", climate_var, sep=""))
)
lat_long <- coordinates(counties3)
#labels <- paste(counties3$MONTHCOMBO, as.character(round(counties3$CORRELATION, 2)), sep = "<br/>")
#counties3$CORRELATION <- as.numeric(levels(counties3$CORRELATION))[counties3$CORRELATION]
counties3a <- data.frame(counties3)
labs <- lapply(seq(nrow(counties3a)), function(i) {
paste0(as.character(round(counties3a[i, "CORRELATION"],2)), '<br/>',
counties3a[i, "MONTHCOMBO"])
})
map <- leaflet(data = counties3) %>%
addProviderTiles(providers$Hydda.Base) %>%
addProviderTiles(providers$Stamen.TonerLines) %>%
#addControl(title, position = "topleft", className="map-title") %>%
fitBounds(exte[1], exte[3], exte[2], exte[4]) %>% addPolygons(fillColor = ~pal(counties3$CORRELATION), fillOpacity = .8, weight = 2, stroke = TRUE, smoothFactor = 0.5, color = "black") %>%
addLabelOnlyMarkers(data = counties3, lng = lat_long[,1], lat = lat_long[,2], label = lapply(labs, HTML), labelOptions = labelOptions(noHide = TRUE, direction = 'middle', textOnly = TRUE, textsize = "14px", col = "white", style=list(
'font-family'= 'serif',
'font-style'= 'bold',
'font-size' = '14px'
))) %>%
addLegend(pal = pal, values = counties3$CORRELATION, labels = c("1", "2"), opacity = .5, title = paste("Correlation", " Matrix", sep="<br>"),
position = "bottomright")
mapStep 3.1. Precipitation time lagged correlations to wheat/drought insurance loss, per county, for the iPNW
panel.cor <- function(x, y, digits = 2, prefix = "", cex.cor, ...)
{
usr <- par("usr"); on.exit(par(usr))
par(usr = c(0, 1, 0, 1))
r <- abs(cor(x, y))
txt <- format(c(r, 0.123456789), digits = digits)[1]
txt <- paste0(prefix, txt)
if(missing(cex.cor)) cex.cor <- 0.8/strwidth(txt)
text(0.5, 0.5, txt, cex = cex.cor * r)
}
var_commodity <- "WHEAT"
var_damage <- "Drought"
#load wheat pricing
#barley <- read.csv("/dmine/data/USDAprices/barleyprices_1988_2017.csv", header=TRUE, strip.white =TRUE)
wheatprice <- read.csv("/dmine/data/USDAprices/wheatprices_1998_2017.csv", header=TRUE, strip.white =TRUE)
wheatprice_year <- aggregate(wheatprice$Price, list(wheatprice$Year), FUN="mean")
colnames(wheatprice_year) <- c("year", "price")
wheatprice <- read.csv("/dmine/data/USDAprices/wheat_prices_revised_1989_2015.csv", header=TRUE, strip.white =TRUE)
wheatprice <- wheatprice[-1]
colnames(wheatprice) <- c("year", "price")
#--accessing output of design matrix/time lag data based on monthly selection from dashboard runs
var1 <- read.csv("/dmine/data/USDA/agmesh-scenarios/Allstates/climatematrix_outputs/pr_jun2_cube_root_loss_climatecorrelation.csv")
colnames(var1)[9] <- paste(colnames(var1)[2], "_zscore", sep="")
var2 <- read.csv("/dmine/data/USDA/agmesh-scenarios/Allstates/climatematrix_outputs/pet_jul3_cube_root_loss_climatecorrelation.csv")
colnames(var2)[9] <- paste(colnames(var2)[2], "_zscore", sep="")
var3 <- read.csv("/dmine/data/USDA/agmesh-scenarios/Allstates/climatematrix_outputs/tmmx_jul2_cube_root_loss_climatecorrelation.csv")
colnames(var3)[9] <- paste(colnames(var3)[2], "_zscore", sep="")
var4 <- read.csv("/dmine/data/USDA/agmesh-scenarios/Allstates/climatematrix_outputs/pr_jun2_cube_root_acres_climatecorrelation.csv")
colnames(var4)[9] <- paste(colnames(var4)[2], "_zscore", sep="")
var5 <- read.csv("/dmine/data/USDA/agmesh-scenarios/Allstates/climatematrix_outputs/pet_jun2_loss_per_acre_climatecorrelation.csv")
colnames(var5)[9] <- paste(colnames(var5)[2], "_zscore", sep="")
var6 <- read.csv("/dmine/data/USDA/agmesh-scenarios/Allstates/climatematrix_outputs/tmmx_jun1_cube_root_acres_climatecorrelation.csv")
colnames(var6)[9] <- paste(colnames(var6)[2], "_zscore", sep="")
var7 <- read.csv("/dmine/data/USDA/agmesh-scenarios/Allstates/climatematrix_outputs/pr_sep5_loss_climatedata.csv")
colnames(var7)[9] <- paste(colnames(var7)[2], "_zscore", sep="")
var8 <- read.csv("/dmine/data/USDA/agmesh-scenarios/Allstates/climatematrix_outputs/pet_sep5_loss_climatedata.csv")
colnames(var8)[9] <- paste(colnames(var8)[2], "_zscore", sep="")
var9 <- read.csv("/dmine/data/USDA/agmesh-scenarios/Allstates/climatematrix_outputs/tmmx_jul2_loss_climatedata.csv")
colnames(var9)[9] <- paste(colnames(var9)[2], "_zscore", sep="")
var9x <- read.csv("/dmine/data/USDA/agmesh-scenarios/Allstates/climatematrix_outputs/fm100_jul3_cube_root_loss_climatedata.csv")
colnames(var9x)[9] <- paste(colnames(var9x)[2], "_zscore", sep="")
var10x <- read.csv("/dmine/data/USDA/agmesh-scenarios/Allstates/climatematrix_outputs/fm1000_aug2_cube_root_loss_climatedata.csv")
colnames(var10x)[9] <- paste(colnames(var10x)[2], "_zscore", sep="")
var11x <- read.csv("/dmine/data/USDA/agmesh-scenarios/Allstates/climatematrix_outputs/pdsi_sep4_cube_root_loss_climatedata.csv")
colnames(var11x)[9] <- paste(colnames(var11x)[2], "_zscore", sep="")
var12x <- read.csv("/dmine/data/USDA/agmesh-scenarios/Allstates/climatematrix_outputs/pdsi_sep4_cube_root_loss_climatecorrelation.csv")
colnames(var12x)[9] <- paste(colnames(var12x)[2], "_zscore", sep="")
var7a <- read.csv("/dmine/data/USDA/agmesh-scenarios/Allstates/climatematrix_outputs/pr_sep5_loss_climatecorrelation.csv")
colnames(var7a)[9] <- paste(colnames(var7a)[2], "_zscore", sep="")
var8a <- read.csv("/dmine/data/USDA/agmesh-scenarios/Allstates/climatematrix_outputs/pet_sep5_loss_climatecorrelation.csv")
colnames(var8a)[9] <- paste(colnames(var8a)[2], "_zscore", sep="")
var9a <- read.csv("/dmine/data/USDA/agmesh-scenarios/Allstates/climatematrix_outputs/tmmx_jul2_loss_climatecorrelation.csv")
colnames(var9a)[9] <- paste(colnames(var9a)[2], "_zscore", sep="")
data1 <- cbind(var1, var2[9], var3[9])
data2 <- cbind(var1[1:6], var2[2], var3[2])
data3 <- cbind(var4[1:6], var5[2], var6[2])
data3 <- plyr::join(data3, wheatprice_year, by = "year")
data4 <- cbind(var7[1:6], var8[2], var9[2], var9x[2], var10x[2], var11x[2], var12x[3] )
#data4$prpet <- data4$pr / data4$pet
data4a <- left_join(data4, var7a, by = c("year" = "year", "county" = "county"))
data4a <- left_join(data4a, wheatprice_year, by = c("year"))
data4aa <- na.omit(data4a)
colnames(data4aa) <- c("X", "pr", "year", "pr_zscore", "damagecause", "county", "pet", "tmmx", "fm100", "fm1000", "pdsi", "cube_loss", "X.y", "pr2", "loss", "state", "commodity", "matrixnumber", "clim_zscore", "loss_zscore", "price")
data4aa$prpet <- data4aa$pr / data4aa$pet
data4aa <- subset(data4aa, , c(year, damagecause, county, commodity, state, matrixnumber, cube_loss, pr, pdsi, pet, tmmx, fm100, fm1000, price, prpet))
write.csv(data4aa, file = "/dmine/data/USDA/agmesh-scenarios/Allstates/summaries/lag_palouse1.csv")
data4aa$pr_scaled <- scale(data4aa$pr, scale = TRUE, center = TRUE)
data4aa$tmmx_scaled <- scale(data4aa$tmmx, scale = TRUE, center = TRUE)
data4aa$pet_scaled <- scale(data4aa$pet, scale = TRUE, center = TRUE)
data4aa$price_scaled <- scale(data4aa$price, scale = TRUE, center = TRUE)
#percentage acreage by county and year, per state
library(plotrix)
library(ggplot2)
library(gridExtra)
setwd("/dmine/data/USDA/agmesh-scenarios/Idaho/summaries")
temp = list.files(pattern = "_summary")
myfiles = lapply(temp, read.csv, header = TRUE)
ziggy.df <- do.call(rbind , myfiles)
ID1 <- aggregate(.~countyfips + year, ziggy.df, sum)
ID1 <- aggregate(.~countyfips, ID1, mean)
setwd("/dmine/data/USDA/agmesh-scenarios/Washington/summaries")
temp = list.files(pattern = "_summary")
myfiles = lapply(temp, read.csv, header = TRUE)
ziggy.df <- do.call(rbind , myfiles)
WA1 <- aggregate(.~countyfips + year, ziggy.df, sum)
WA1 <- aggregate(.~countyfips, WA1, mean)
setwd("/dmine/data/USDA/agmesh-scenarios/Oregon/summaries")
temp = list.files(pattern = "_summary")
myfiles = lapply(temp, read.csv, header = TRUE)
ziggy.df <- do.call(rbind , myfiles)
OR1 <- aggregate(.~countyfips + year, ziggy.df, sum)
OR1 <- aggregate(.~countyfips, OR1, mean)
countiesfips <- read.csv("/dmine/data/counties/counties_fips.csv", header = TRUE)
colnames(countiesfips) <- c("countyfips", "county", "state")
OR2 <- merge(countiesfips, OR1, by=("countyfips"))
ID2 <- merge(countiesfips, ID1, by=("countyfips"))
WA2 <- merge(countiesfips, WA1, by=("countyfips"))
#--OR
setwd("/dmine/data/USDA/agmesh-scenarios/Allstates/")
#setwd(monthdir2)
temp = list.files(pattern = paste("Oregon", "_summary", sep=""))
myfiles = lapply(temp, read.csv, header = TRUE)
ziggy.df <- do.call(rbind , myfiles)
xrange <- as.data.frame(ziggy.df)
xrange$county <- as.character(xrange$county)
xrange$damagecause <- as.character(xrange$damagecause)
xrange_1989 <- subset(xrange, year <= 2000)
xrange_2015 <- subset(xrange, year >= 2001)
xrange_1989$loss <- xrange_1989$acres
xrange_1989$acres <- NA
xrange <- rbind(xrange_2015)
xrange$commodity <- trimws(xrange$commodity)
xrange$county <- trimws(xrange$county)
xrange$damagecause <- trimws(xrange$damagecause)
#--acres for all damage causes aggregated
OR_loss_commodity <- subset(xrange, commodity == var_commodity)
OR_loss_all <- aggregate(OR_loss_commodity$acres, by=list(OR_loss_commodity$year, OR_loss_commodity$county, OR_loss_commodity$state), FUN = "sum")
colnames(OR_loss_all) <- c("year", "county", "state", "all_damagecause_acres")
OR_loss1 <- subset(xrange, commodity == var_commodity & damagecause == var_damage)
OR_loss2 <- aggregate(OR_loss1$loss, by=list(OR_loss1$year, OR_loss1$county, OR_loss1$state), FUN = "sum")
OR_loss2_acres <- aggregate(OR_loss1$acres, by=list(OR_loss1$year, OR_loss1$county, OR_loss1$state), FUN = "sum")
colnames(OR_loss2) <- c("year", "county", "state", "loss")
colnames(OR_loss2_acres) <- c("year", "county", "state", "acres")
OR_loss2$acres <- OR_loss2_acres$acres
OR_loss2$lossperacre <- OR_loss2$loss / OR_loss2$acres
OR_loss_climate <- merge(OR_loss2, OR2[-4], by=c("county", "state"))
OR_loss_climate_2 <- merge(OR_loss_all, OR_loss_climate, by=c("year", "county", "state"))
#-WA
setwd("/dmine/data/USDA/agmesh-scenarios/Allstates/")
#setwd(monthdir2)
temp = list.files(pattern = paste("Washington", "_summary", sep=""))
myfiles = lapply(temp, read.csv, header = TRUE)
ziggy.df <- do.call(rbind , myfiles)
xrange <- as.data.frame(ziggy.df)
xrange$county <- as.character(xrange$county)
xrange$damagecause <- as.character(xrange$damagecause)
xrange_1989 <- subset(xrange, year <= 2000)
xrange_2015 <- subset(xrange, year >= 2001)
xrange_1989$loss <- xrange_1989$acres
xrange_1989$acres <- NA
xrange <- rbind(xrange_2015)
xrange$commodity <- trimws(xrange$commodity)
xrange$county <- trimws(xrange$county)
xrange$damagecause <- trimws(xrange$damagecause)
#--acres for all damage causes aggregated
WA_loss_commodity <- subset(xrange, commodity == var_commodity)
WA_loss_all <- aggregate(WA_loss_commodity$acres, by=list(WA_loss_commodity$year, WA_loss_commodity$county, WA_loss_commodity$state), FUN = "sum")
colnames(WA_loss_all) <- c("year", "county", "state", "all_damagecause_acres")
WA_loss1 <- subset(xrange, commodity == var_commodity & damagecause == var_damage)
WA_loss2 <- aggregate(WA_loss1$loss, by=list(WA_loss1$year, WA_loss1$county, WA_loss1$state), FUN = "sum")
WA_loss2_acres <- aggregate(WA_loss1$acres, by=list(WA_loss1$year, WA_loss1$county, WA_loss1$state), FUN = "sum")
colnames(WA_loss2) <- c("year", "county", "state", "loss")
colnames(WA_loss2_acres) <- c("year", "county", "state", "acres")
WA_loss2$acres <- WA_loss2_acres$acres
WA_loss2$lossperacre <- WA_loss2$loss / WA_loss2$acres
WA_loss_climate <- merge(WA_loss2, WA2[-4], by=c("county", "state"))
WA_loss_climate_2 <- merge(WA_loss_all, WA_loss_climate, by=c("year", "county", "state"))
#-ID
setwd("/dmine/data/USDA/agmesh-scenarios/Allstates/")
#setwd(monthdir2)
temp = list.files(pattern = paste("Idaho", "_summary", sep=""))
myfiles = lapply(temp, read.csv, header = TRUE)
ziggy.df <- do.call(rbind , myfiles)
xrange <- as.data.frame(ziggy.df)
xrange$county <- as.character(xrange$county)
xrange$damagecause <- as.character(xrange$damagecause)
xrange_1989 <- subset(xrange, year <= 2000)
xrange_2015 <- subset(xrange, year >= 2001)
xrange_1989$loss <- xrange_1989$acres
xrange_1989$acres <- NA
xrange <- rbind(xrange_2015)
xrange$commodity <- trimws(xrange$commodity)
xrange$county <- trimws(xrange$county)
xrange$damagecause <- trimws(xrange$damagecause)
#--acres for all damage causes aggregated
ID_loss_commodity <- subset(xrange, commodity == var_commodity)
ID_loss_all <- aggregate(ID_loss_commodity$acres, by=list(ID_loss_commodity$year, ID_loss_commodity$county, ID_loss_commodity$state), FUN = "sum")
colnames(ID_loss_all) <- c("year", "county", "state", "all_damagecause_acres")
ID_loss1 <- subset(xrange, commodity == var_commodity & damagecause == var_damage)
ID_loss2 <- aggregate(ID_loss1$loss, by=list(ID_loss1$year, ID_loss1$county, ID_loss1$state), FUN = "sum")
ID_loss2_acres <- aggregate(ID_loss1$acres, by=list(ID_loss1$year, ID_loss1$county, ID_loss1$state), FUN = "sum")
colnames(ID_loss2) <- c("year", "county", "state", "loss")
colnames(ID_loss2_acres) <- c("year", "county", "state", "acres")
ID_loss2$acres <- ID_loss2_acres$acres
ID_loss2$lossperacre <- ID_loss2$loss / ID_loss2$acres
ID_loss_climate <- merge(ID_loss2, ID2[-4], by=c("county", "state"))
ID_loss_climate_2 <- merge(ID_loss_all, ID_loss_climate, by=c("year", "county", "state"))
merged_iPNW <- rbind(ID_loss_climate_2, WA_loss_climate_2, OR_loss_climate_2)
merged_iPNW$pct_acreage <- merged_iPNW$acres / merged_iPNW$all_damagecause_acres
#
#--plotting results of individual variables
pairs(cube_loss ~ pr_scaled + tmmx_scaled + pet_scaled, data = data4aa, col = data4aa$state,
lower.panel=panel.smooth, upper.panel=panel.cor, main = "initial pairs plot")
par(mar=c(12.7, 6.1, 4, 2.1), family = 'serif', mgp=c(4, 1, 0), las=0)
data4aaaa <- merge(data4aa, merged_iPNW, by = c("state", "county", "year"))
ggplot(data4aaaa, aes(pet_scaled, cube_loss, color = state)) +
geom_point(aes(size = price)) +
stat_smooth(aes(group = 1), method = "loess") + labs(x = "Scaled Potential Evapotranspiration (mm)", y = "Cube root loss ($)") + theme(axis.title.y = element_text(family = "Serif", size=16)) + theme(axis.title.x = element_text(family = "Serif", size=16), axis.text.x = element_text(size=rel(1.5), hjust = 1, family = "Serif"), axis.text.y = element_text(size=rel(1.5), hjust = 1, family = "Serif"))
ggplot(data4aaaa, aes(pr_scaled, cube_loss, color = state)) +
geom_point(aes(size = price)) +
stat_smooth(aes(group = 1), method = "loess") + labs(x = "Scaled Precipitation (mm)", y = "Cube root loss ($)") + theme(axis.title.y = element_text(family = "Serif", size=16)) + theme(axis.title.x = element_text(family = "Serif", size=16), axis.text.x = element_text(size=rel(1.5), hjust = 1, family = "Serif"), axis.text.y = element_text(size=rel(1.5), hjust = 1, family = "Serif"))
ggplot(data4aaaa, aes(tmmx_scaled, cube_loss, color = state)) +
geom_point(aes(size = price)) +
stat_smooth(aes(group = 1), method = "loess") + labs(x = "Scaled Max Temperature (C)", y = "Cube root loss ($)") + theme(axis.title.y = element_text(family = "Serif", size=16)) + theme(axis.title.x = element_text(family = "Serif", size=16), axis.text.x = element_text(size=rel(1.5), hjust = 1, family = "Serif"), axis.text.y = element_text(size=rel(1.5), hjust = 1, family = "Serif"))
ggplot(data4aaaa, aes(prpet, cube_loss, color = state)) +
geom_point(aes(size = price)) +
stat_smooth(aes(group = 1), method = "loess") + labs(x = "Aridity Index (PR / PET)", y = "Cube root loss ($)") + theme(axis.title.y = element_text(family = "Serif", size=16)) + theme(axis.title.x = element_text(family = "Serif", size=16), axis.text.x = element_text(size=rel(1.5), hjust = 1, family = "Serif"), axis.text.y = element_text(size=rel(1.5), hjust = 1, family = "Serif"))